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Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation. [ABSTRACT FROM AUTHOR]

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (BLUP) of Goldberger in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed PUP is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals. [ABSTRACT FROM AUTHOR]

Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of the errors is correctly specified. In this comment, we first discuss how the assumptions imposed on the errors depend on the model and estimator adopted. This enables researchers to assess the validity of the assumptions imposed on the structure of the errors, and the relevant information set for conditional analyses. We then propose a simple sensitivity analysis in order to quantify the amount of misspecification on the dependence structure of the errors required for the conclusions of conditional analyses to be changed. [ABSTRACT FROM AUTHOR]

Abstract.Experimenters have limited flexibility in their choice of cross-over designs, in terms of, number of treatments, subjects, and repeated measurements. Also, cross-over design balanced with higher-order carryover effects presented in the literature requires a large number of experimental subjects, which is impractical for its implementation. Most experimental studies demand a smaller number of subjects, which is possible only in a minimally balanced cross-over design. In this article, two simple methods have been presented, which construct minimal balanced cross-over designs having less, equal, and more periods than the number of treatments. The first method constructs cross-over designs with more periods, while the second method constructs cross-over designs with less or equal periods. As a result, four new families of cross-over designs have been obtained, in which one family of cross-over designs has periods greater than the number of treatments, two families of cross-over designs have three periods, and one family of cross-over designs has periods equal to the number of treatments. All the designs possess good efficiency of separability, and therefore they are suitable for the estimation of treatments and first-second carryover effects of treatments at lower cost of experiments due to a smaller number of experimental subjects as compared to the designs available in the literature. [ABSTRACT FROM AUTHOR]

This paper compares water and sanitation services in municipalities that entered into a concession arrangement with a private operator versus those in a comparable control group that continued with a public operator. We explore five variables of interest: average tariff; water coverage; sewage collection; sewage treatment; and water losses. Our empirical strategy improves on existing techniques: after controlling for municipality idiosyncrasies, we adopt a difference-in-differences model with nearest-neighbor matching to evaluate private sector management impacts on these variables. We find a large drop in tariff after the concession switching to a private operator, but tariff increases are greater than public operator during the first four years. There is economical and statistical evidence of increasing sewage collection and treatment over time, but only economical evidence of improvements in water coverage; and no convincing evidence of reduction in water losses. The results suggest huge positive economic impact of switching services from public to private. [ABSTRACT FROM AUTHOR]

AbstractInterference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included. [ABSTRACT FROM AUTHOR]

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable. [ABSTRACT FROM AUTHOR]

To assess the effect of treatment in survival studies, a randomized controlled trial (RCTs) is usually designed. Due to disease progression or other safety reasons, patients in the standard group may be allowed to switch to the experimental treatment. In such cases, estimation of the treatment effect is inevitably performed using naive methods such as intention-to-treat (ITT), per-protocol (PP), or more advanced methods such as the rank-preserving structural failure time (RPSFT) models or iterative parameter estimation (IPE). In some trials, people who progress early might be more likely to switch, and in other trials, people who progress late might be more likely to switch – depending on the characteristics of the disease and the drug being examined. So, this study simulated a new range of realistic scenarios with regard to patients' prognosis and investigated the performance of the adjusted methods in the early and late switching with respect to the median of the survival time. [ABSTRACT FROM AUTHOR]

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development. [ABSTRACT FROM AUTHOR]

A mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture model accounts for the fact that not all the patients in the treated group will respond to the treatment and consequently their responses follow the same distribution as the responses from untreated patients. The treatment effect in this context consists of both the fraction of the treated patients that are responders and the magnitude of the shift in the distribution for the responders. In this article, we investigate asymptotic properties of method of moment estimators for the treatment effect based on a semi-supervised two-component mixture model. From these properties, we develop asymptotic confidence intervals and demonstrate their superior statistical inference performance compared to the computationally intensive bootstrap intervals and their Bias-Corrected versions. [ABSTRACT FROM AUTHOR]

We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai, Li and Zou study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee. A companion R package rdcqr is developed. [ABSTRACT FROM AUTHOR]

This article develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to impose exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the resulting treatment effect estimates are automatically weight normalized and exhibit both low bias and reduced variance in finite samples compared to conventional inverse probability weighting methods. We provide conditions for asymptotic normality and semiparametric efficiency and demonstrate how to use additional information about the treatment selection step for bias reduction in finite samples. A doubly robust extension is proposed as well. Monte Carlo simulations suggest that the theoretical advantages translate well to finite samples. The method is illustrated in an empirical example. [ABSTRACT FROM AUTHOR]

How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates X on a real-valued response Y. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (P = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, P = 0.047) and negative effects (e.g., Magdalena, P = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online. [ABSTRACT FROM AUTHOR]

This study estimates the joint effect of a new political regime and war against Iraq, on Iran's per capita Gross Domestic Product ('GDP,' constant 2010 US$) for the period 1978–1988, during the revolution/war. I use a synthetic control approach, whereby a synthetic Iran is constructed as a weighted average of other Middle East and North Africa ('MENA')/Organization of the Petroleum Exporting ('OPEC') countries to match the average level of some key per capita GDP correlates over the period 1970–1977 as well as the evolution of the actual Iranian per capita GDP during that period. I find a sizable negative effect of the joint treatment. The average Iranian lost an accumulated sum of approximately US$ 34,660 during 1978–1988 (i.e. the average annual real per capita income loss of US$ 3,150). This loss equals 40% of the real income per capita, which an Iranian could earn in the absence of revolution and war. The confidence sets based on constant, linear, and uniform assumptions of treatment effect show that estimated income loss for Iran is sizeable and statistically significant. The results remain robust to a set of placebo tests. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the financial benefits that can result from effective sexual offender treatment. We base our estimates on both an earlier outcome evaluation of a prison-based sexual offender programme and a report of the costs to the judicial system of reoffending. The outcome study reported significant reductions in re-offence rates compared to a matched group of untreated sexual offenders. We show that these reductions in reoffending not only saved a significant number of people from subsequently being abused by the offenders, it also resulted in very significant savings to the taxpayers to the tune of over $14 million Canadian. While these results are encouraging, additional studies are needed to extend the examination of these potential benefits. [ABSTRACT FROM AUTHOR]

While literature is clear on the role of finance in small business survival and growth, the relative importance of formal and informal credit in small business growth remains contentious. While earlier studies report the importance of formal credit in driving growth of firms of all sizes, recent studies find informal credit to drive the growth of only small businesses. The aim of this paper is therefore to contribute to the debate by bringing the context of four developing economies in the Southern African Developing Community (SADC) using the FinScope surveys on 10,830 Micro Small and Medium Enterprises (henceforth MSMEs). To address selection bias caused by various factors affecting small business participation in the formal/informal credit market, we used propensity score matching (PSM) where we paired small businesses from the two groups. We examined the effect of access to formal/informal credit on business performance using Average Treatment Effect (ATE) as well as Average Treatment effect on the Treated (ATT). Our results suggest that firms accessing formal credit tend to exhibit better performance in Zimbabwe but a similar performance gain was not observed in the rest of the countries. For informal credit, our results show that firms accessing informal credit tend to perform less than those without access, suggesting value depletion effect of informal credit. [ABSTRACT FROM AUTHOR]

We propose a simple-to-implement dynamic panel difference-in-difference method to assess the impact of (place-based) macro policies. The idea is to exploit both serial correlation and cross-sectional dependence within a panel and implement a semi- difference-in-difference decomposition. Different from existing panel data methods, the proposed approach is easy to implement and conduct statistical inference and can incorporate multiple treated groups at different time thresholds. Monte Carlo simulations show the proposed estimator performs reasonably well with finite samples. An application to assess the general impact of a free trade zone policy is also provided. [ABSTRACT FROM AUTHOR]

Crossover designs balanced in first and second order carryover effects discussed in literature often require large number of experimental units. In practice experiments demand fewer experimental units where minimal balanced crossover designs are desirable. In this paper, a simple method of construction of minimal balanced crossover designs through a new form of terrace is presented. A new form of terrace constructs one series of crossover designs having less number of periods than the number of treatments and two series of crossover designs having more periods than the treatments. Crossover designs possess good efficiency of separability and optimal for the estimation of treatment effects. [ABSTRACT FROM AUTHOR]

This paper evaluates the short-term labour market impact of the COVID-19 containment measures in Germany. By assessing the treatment effect on unemployment via difference-in-difference estimation, we find that 60% of the considerably increased inflows from employment into unemployment in April 2020 were due to the shutdown measures. Disentangling further, we find that the hiring margin accounted for additional 82% of the unemployment effect coming from the separations margin. In sum, the shutdown measures increased unemployment in the short run by 117,000 persons. [ABSTRACT FROM AUTHOR]

Quantile treatment effects are often considered in a quantile regression framework to adjust for the effect of covariates. In this study, we focus on the problem of testing whether the treatment effect is significant at a set of quantile levels (e.g. lower quantiles). We propose a regional quantile regression rank test as a generalisation of the rank test at an individual quantile level. This test statistic allows us to detect the treatment effect for a prespecified quantile interval by integrating the regression rank scores over the region of interest. A new model-based bootstrap method is constructed to estimate the null distribution of the test statistic. A simulation study is conducted to demonstrate the validity and usefulness of the proposed test. We also demonstrate the use of the proposed method through an analysis of the 2016 US birth weight data and selected S&P 500 sector portfolio data. [ABSTRACT FROM AUTHOR]

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic's spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (p-value: <0.001) and mild interventions (p-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, then epidemic would be under control effectively and early. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Data for 30-day readmission rates in American hospitals often show that patients that receive Home Health Care (HHC) have a higher probability of being readmitted to hospital than those that did not receive such services, but it is expected that when control variables are included in a regression we will obtain a "sign reversal" of the treatment effect. We map the real-world situation to the binary logistic regression model, and we construct a counterfactual probability metric that leads to necessary and sufficient conditions for the sign reversal to occur, conditions that show that logistic regression is an appropriate tool for this research purpose. This metric also permits us to obtain evidence related to the criteria used to assign HHC treatment. We examine seven data samples from different USA hospitals for the period 2011–2017. We find that in all cases the provision of HHC increased the probability of readmission of the treated patients. This casts doubt on the appropriateness of the 30-day readmission rate as an indicator of hospital performance and a criterion for hospital reimbursement, as it is currently used for Medicare patients. [ABSTRACT FROM AUTHOR]

The physical problems associated with fistula result in psychological alteration. This study was aimed to assess whether psychological wellbeing of women with fistula after surgical treatment differ from that of before treatment. Institution-based pre-post follow-up design was conducted. Self-reported incontinence and psychological wellbeing score were assessed. Data were analyzed using Stata 12. Paired t-test and multiple linear regressions were fitted to identify variables correlate with the score difference. In this study, 117 women were interviewed giving response rate of 98.3 percent. Their mean (±sd) age was 27.13 ± 5.37 years. On admission, all women had urinary or fecal incontinence and their mean psychological score was 31.1 (95 percent CI; 30.5–31.6). After treatment, however, 89.7 percent (n = 105) of women reported that they regained continence. Similarly, the mean psychological score has dropped to 18.05 (95 percent CI; 16.4–21.6, p <.001). Pre-treatment score, normal body weight, regaining continence, and getting counseling service were correlates of score difference. Psychological wellbeing of women with genital fistula was improved drastically after treatment. Establishing a system for early identification and treatment of cases could reduce prolonged psychological alteration. [ABSTRACT FROM AUTHOR]

This paper was analyzed the impact of off-farm activities on food security status of rural households in the Dibatie district of the Bebishangul Gumuz region. The study was used in a simple random sampling technique to select respondent households with proportionate sample size based on the number of households exist in sampled kebele administrations. The primary data were collected using a structured questionnaire and key informants' interview. Both descriptive statistics and econometric model (an endogenous switching probit) were used to analyze this data at household levels. This model accounts for selection bias resulting from unobserved factors that potentially affect both household's participation and food security outcomes and also the model shows the counterfactual case of food security outcomes. Food security index is used to measure the food security status of sample households based on average kCal/day/adult equivalent. The result of this study revealed that about 59.4% of the households were food secure and 40.6% of households were food insecure. And also, about 55.4% of the households were off-farm participants and 44.6% of households were off-farm non-participants. Moreover, computing switch_probit command, the impacts of participant household have higher probability of food security status as compared with the counterfactual case of non-participants. In addition, the model result shows that variables such as household size, distance to the nearest market, age of household head, education level of household head, special skill, utilization of formal credit, access to electricity and frequency of extension contact were found to have a significant influence on off-farm participation. Finally, the study suggesting that the interventions designed to promote farmers participating in off-farm activities that are made to increase food security status at the household level in the study area. This is because there could be increased productivity effects in this off-farm economy, better agricultural linkages and indeed, stronger structural transformation of the rural economy. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to explore the effectiveness of a five-week Novafon local vibration voice therapy (NLVVT) program for dysphonia treatment. Methods: Eleven dysphonic subjects participated in this specific program. Results: Treatment effects were assessed during (i.e. weekly) and after NLVVT. Large and significant treatment effects were revealed in acoustics (i.e. spectrography), and multiparametric indices (i.e. Acoustic Voice Quality Index (AVQI), and Dysphonia Severity Index (DSI)) during and after NLVVT (all p values <.01). Additionally, self-evaluation (i.e. Voice Handicap Index (VHI)) showed a significant improvement after NLVVT (p <.01). Gender independent voice range profile parameters (i.e. acoustics) only showed significant effects after treatment (p ≤.01), but not during the treatment. Finally, aerodynamic measurement (i.e. phonation quotient) showed low treatment effects after NLVVT, which were not significant (p >.05). Conclusions: The preliminary results showed that NLVVT might be successful in voice treatment. Large treatment effects might be expected in AVQI, DSI, spectrography and VHI after using NLVVT. Other voice characteristics showed smaller treatment effects (i.e. voice range profile parameters) or no meaningful treatment effects (i.e. phonation quotient). [ABSTRACT FROM AUTHOR]

We consider a control group versus treatment group experimental design and assert that powering the design for a potential treatment effect that is represented by a pure shift of the control group distribution is usually unrealistic. Instead, we propose the use of a mixture model as the design alternative in anticipation that there might be a sub-population in the treated population whose responses come from the same control group distribution. When the responses in the treatment group follow a mixture model, the sample size found by the traditional pure shift alternative based method is demonstrated to be under-powered. We develop a new sample size formula for the Wilcoxon test statistic and propose a more general definition of the treatment effect. Method of moment estimators of the treatment effect are proposed and their bias and mean squared error properties are evaluated. [ABSTRACT FROM AUTHOR]

This study develops methods for conducting uniform inference on quantile treatment effects for sharp regression discontinuity designs. We develop a score test for the treatment significance hypothesis and Wald-type tests for the hypotheses related to treatment significance, homogeneity, and unambiguity. The bias from the nonparametric estimation is studied in detail. In particular, we show that under some conditions, the asymptotic distribution of the score test is unaffected by the bias, without under-smoothing. For situations where the conditions can be restrictive, we incorporate a bias correction into the Wald tests and account for the estimation uncertainty. We also provide a procedure for constructing uniform confidence bands for quantile treatment effects. As an empirical application, we use the proposed methods to study the effect of cash-on-hand on unemployment duration. The results reveal pronounced treatment heterogeneity and also emphasize the importance of considering the long-term unemployed. [ABSTRACT FROM AUTHOR]

In 2011 a wave of revolutionary movements, the so-called Arab Spring, spread in the Middle East and North Africa. Libya was one of the most affected countries, ending Gaddafi's dictatorship after an international intervention and a civil war. This paper assesses the effects that this revolution had on Libyan economy. The analysis is made by means of the synthetic control method. Our estimates for the 2011–2014 period show (i) a cumulative loss in the growth rate of per capita real GDP of 64.15%; (ii) a cumulative loss in per capita real GDP of 56,548 dollars; and (iii) a cumulative loss in the aggregate real GDP of 350.5 billion dollars. [ABSTRACT FROM AUTHOR]

Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to nonidentification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental interventions that shift propensity score values rather than set treatments to fixed values. Incremental interventions have several crucial advantages. First, they avoid positivity assumptions entirely. Second, they require no parametric assumptions and yet still admit a simple characterization of longitudinal effects, independent of the number of timepoints. For example, they allow longitudinal effects to be visualized with a single curve instead of lists of coefficients. After characterizing incremental interventions and giving identifying conditions for corresponding effects, we also develop general efficiency theory, propose efficient nonparametric estimators that can attain fast convergence rates even when incorporating flexible machine learning, and propose a bootstrap-based confidence band and simultaneous test of no treatment effect. Finally, we explore finite-sample performance via simulation, and apply the methods to study time-varying sociological effects of incarceration on entry into marriage. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper deals with the identification of treatment effects using difference-in-differences estimators when several pretreatment periods are available. We define a family of identifying nonnested assumptions that lead to alternative difference-in-differences estimators. We show that the most usual difference-in-differences estimators imply equivalence conditions for the identifying nonnested assumptions. We further propose a model that can be used to test multiple equivalence conditions without imposing any of them. We conduct a Monte Carlo analysis and apply our approach to several recent papers to show its practical relevance. [ABSTRACT FROM AUTHOR]

Background: Nevus of Ota can be treated successfully using the Q-switched (QS) laser; but few studies are long-term retrospective of the efficacy and influencing factor, studies on the recurrence is even less. Purpose: Retrospective analysis of the clinical characteristics and recurrence of QS Nd:YAG laser treatment of nevus of Ota in 224 Chinese patients at Laser Cosmetology Center of Department of Dermatology, the Second Hospital, Xi'an Jiaotong University. Methods: The data of 224 patients with nevus of Ota were analyzed retrospectively, which included the correlation among lesion color, treatment sessions, gender, age, lesion types and effect, and all patients were followed up for 2-10 years. Results: Higher number of treatment sessions that were conducted using the QS Nd:YAG laser system was positively associated with better responses to treatment. 99.11% of patients acquired more than 75% improvement with an average of 3.7 sessions. Only eight patients (3.57%) who had been completely cleared developed recurrence at the same site as before. Conclusion: The treatment of nevus of Ota with QS Nd:YAG laser is safe and effective with rare recurrence. [ABSTRACT FROM AUTHOR]

Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov-Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in small to moderately sized samples. As an empirical illustration, we apply our test to a dataset that has been used in the literature to estimate the return on college education in China, to check whether the assumption of CMI is supported by the dataset and show the plausibility of the extra symmetry condition that is necessary for this new test. [ABSTRACT FROM AUTHOR]

In this studywe propose a unified semiparametric approach to estimate various indices of treatment effect under the density ratio model, which connects two density functions by an exponential tilt. For each index, we construct two estimating functions based on the model and apply the generalized method of moments to improve the estimates. The estimating functions are allowed to be non smooth with respect to parameters and hencemake the proposed method more flexible.We establish the asymptotic properties of the proposed estimators and illustrate the application with several simulations and two real data sets. [ABSTRACT FROM AUTHOR]

This article shows that, in panel settings when the probability of receiving a treatment is low simple comparisons of outcomes between those who are treated in a given period and those who are untreated in that period, but receive the treatment in some other period, approximately identify the average effect of the treatment on the treated. Monte Carlo simulations reveal that this approximate selection-correction substantially reduces selection bias relative to naive comparisons of outcomes between treated and untreated units. [ABSTRACT FROM AUTHOR]

We consider the construction of one-sided group sequential designs where the stopping rule includes boundaries for early stopping to accept for futility and to reject for efficacy. The traditional assumption that all patients have the same likelihood of benefiting from the treatment is sometimes unrealistic and can underestimate the required sample size. This motivates us to power the design for an alternative where the treatment group observations come from a mixture of normal distributions. For the proposed setting, we use standardized test statistics based on sample means, and the test turns out to be an L-optimal similar test. Stopping boundaries and arm size for the design are determined by Type I and Type II error spending equations. We demonstrate the need for larger arm sizes when trying to detect a mixture alternative compared to trying to detect a pure shift alternative. The unknown variance case is discussed. With the mixture model, we discuss a more general definition of treatment effect. The maximum likelihood estimator for the treatment effect is discussed. [ABSTRACT FROM AUTHOR]

It is known that the naive bootstrap is not asymptotically valid for a matching estimator of the average treatment effect with a fixed number of matches. In this article, we propose asymptotically valid inference methods for matching estimators based on theweighted bootstrap. The key is to construct bootstrap counterparts by resampling based on certain linear forms of the estimators. Ourweighted bootstrap is applicable for the matching estimators of both the average treatment effect and its counterpart for the treated population. Also, by incorporating a bias correction method in Abadie and Imbens (2011), our method can be asymptotically valid even for matching based on a vector of covariates. A simulation study indicates that the weighted bootstrapmethod is favorably comparable with the asymptotic normal approximation. As an empirical illustration, we apply the proposed method to the National SupportedWork data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In observational studies for the interaction between treatments, one needs to estimate and present both the treatment effects and the interaction to learn the significance of the interaction to the treatment effects. In this article, we estimate the treatment effects and the interaction jointly by using only one logistic model and based on maximum-likelihood. We present the interaction by (1) point estimate and confidence interval of the interaction, (2) point estimate and confidence region of (treatment effect, interaction), and (3) point estimate and confidence interval of the interaction when the maximum-likelihood estimate of one treatment effect falls into specified range. [ABSTRACT FROM AUTHOR]

In many statistical applications, the observed data take the form of sets rather than points. Examples include bracket data in survey analysis, tumor growth and rock grain images in morphology analysis, and noisy measurements on the support function of a convex set in medical imaging and robotic vision. Additionally, in studies of treatment effects, researchers often wish to conduct inference on nonparametric bounds for the effects which can be expressed by means of random sets. This article develops the concept of nonparametric likelihood for random sets and its mean, known as the Aumann expectation, and proposes general inference methods by adapting the theory of empirical likelihood. Several examples, such as regression with bracket income data, Boolean models for tumor growth, bound analysis on treatment effects, and image analysis via support functions, illustrate the usefulness of the proposed methods. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper estimates the treatment effect of grade repetitions in French junior high schools, using a value-added test score as outcome and quarter of birth as instrument. With linear two-stage least squares, local average treatment effect is estimated at around 1.6 times the standard deviation of the achievement gain. With non-linear full-information maximum likelihood, average treatment on the treated ranges between one and one-quarter of the standard deviation of the outcome. Grade repetition in junior high school is also shown to increase the probability of graduating from junior high school by 2.5 probability points. [ABSTRACT FROM PUBLISHER]

Background: Patients' beliefs and attitudes toward a treatment can affect treatment response. In unblinded trials this can affect outcomes. Aims: The aim of this analysis was to examine the association between treatment preference and expectation and outcome in a trial of pain treatments. Methods: In a randomized trial (ISRCTN67013851) of four treatments for chronic widespread pain, participants were asked which they would prefer and what improvement they expect from each. The proportion of participants reporting positive health outcomes at three time points after treatment were compared between those matched or unmatched with their preference and between those with and without expectation for improvement. Odds ratios were calculated adjusted for baseline characteristics associated with preference and expectation. Results: Four hundred forty-two participants were recruited to the trial (69.5% female). The proportion reporting positive outcomes among participants matched to their preference compared to those unmatched was 33.3% vs. 34.4% at the end of treatment (adjusted odds ratio [aOR] = 0.80, 95% confidence interval [CI], 0.44–1.46), 34.4% vs. 29.0% at 3 months (aOR = 1.23, 95% CI, 0.67–2.26), and 34.8% vs. 30.3% at 2 years (aOR = 1.31, 95% CI, 0.70–2.46). The proportion of participants reporting positive outcomes among those expecting improvement compared to those not expecting improvement was 36.6% vs. 15.0% at the end of treatment (aOR = 2.03, 95% CI, 1.07–3.85), 34.1% vs. 13.2% at 3 months (aOR = 2.31, 95% CI, 1.22–4.38), and 32.8% vs. 19.1% at 2 years (aOR = 1.16, 95% CI, 0.67–2.36). Conclusions: Treatment preference had no clear effect on outcomes, but expectation did. These results could inform future approaches to management, and researchers assessing treatments should take into account this expectation effect. [ABSTRACT FROM AUTHOR]

Objective: Threatened preterm labor (tPTL) is a complication of pregnancy. Identification of women and clinical definition differs between countries. This study investigated differences in tPTL and effectiveness of vaginal progesterone to prevent preterm birth (PTB) between two countries.Methods: Secondary analysis of a randomized controlled trial (RCT) from Argentina and Switzerland comparing vaginal progesterone to placebo in women with tPTL (n = 379). Cox proportional hazards analysis was performed to compare placebo groups of both countries and to compare progesterone to placebo within each country. We adjusted for baseline differences. Iatrogenic onset of labor or pregnancy beyond gestational age of interest was censored.Results: Swiss and Argentinian women were different on baseline. Risks for delivery <14 days and PTB < 34 and < 37 weeks were increased in Argentina compared to Switzerland, HR 3.3 (95% CI 0.62-18), 54 (95% CI 5.1-569) and 3.1 (95% CI 1.1-8.4). In Switzerland, progesterone increased the risk for delivery <14 days [HR 4.4 (95% CI 1.3-15.7)] and PTB <37 weeks [HR 2.5 (95% CI 1.4-4.8)], in Argentina there was no such effect.Conclusion: In women with tPTL, the effect of progesterone may vary due to population differences. Differences in populations should be considered in multicenter RCTs. [ABSTRACT FROM AUTHOR]

This study has used the time series data of Australia, China, Denmark, India, Japan, the Netherlands, Norway, the Philippines, South Korea, Spain, and the United States from the World Bank to estimate the agricultural economic shock of Taiwan's accession to the World Trade Organization (WTO). We have adopted the recently developed panel data approach for policy evaluation to construct the annual agricultural growth path of Taiwan, which is mainly based on the cross-sectional correlations between the domestic and international agricultural trade markets in the absence of Taiwan's entry into the WTO. Our results have not only revealed the importance ofex postcounterfactual analysis, but also provided empirical evidence that the agricultural economic shock is not as severe as predicted by manyex antestudies. Based on these results, we have concluded that the outcome may have arisen from the slow progress of achieving agricultural trade liberalization under the WTO and the Taiwanese Government's effective adoption of phase-in periods and relative adjustment policies. [ABSTRACT FROM PUBLISHER]

Objective A diagnosis of autism spectrum disorder ( ASD) is often accompanied by challenging behaviour that may limit the ability to engage in education, employment, or social settings. The purpose of this review was to examine the effectiveness of self-management interventions in reducing challenging behaviours for individuals diagnosed with ASD. This review included self-management interventions that specifically targeted a reduction in challenging behaviours as well as studies that specifically targeted skill acquisition and described improvements in challenging behaviours as a collateral effect of the intervention Method Studies were located by conducting a systematic search of peer-reviewed literature published in English prior to October 2015. The PsycINFO and ERIC databases were searched. A quality assessment was conducted using the What Works Clearinghouse ( WWC) guidelines. A final data set of 12 single-subject research design studies that met quality standards was subsequently analysed. Treatment effect sizes were calculated using the percentage of non-overlapping data. Results Results were analysed by age, gender, symptom severity, presentation of problem behaviour, and the presence of functional behaviour assessments. Results indicated that self-management interventions are effective at decreasing challenging behaviour for children aged four to 18 years who may be described as either high or low functioning. Conclusions Self-management of challenging behaviour was identified as an emerging treatment, and inclusion within a positive behaviour support framework was argued. Important areas for consideration in future research are described. [ABSTRACT FROM AUTHOR]

This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is not necessary to have any continuous variables, none of the variables need to have large support, and the instrument can be binary even if the corresponding endogenous treatment variable and/or outcome is continuous. The outcome can be mismeasured or interval-measured, and the endogenous treatment variable need not even be observed. The identification results are constructive, and can be empirically implemented using standard estimation results. [ABSTRACT FROM PUBLISHER]

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is weak (i.e., when the discontinuity is of a small magnitude), the usual t-test based on the FRD estimator and its standard error suffers from asymptotic size distortions as in a standard instrumental variables setting. This problem can be especially severe in the FRD setting since only observations close to the discontinuity are useful for estimating the treatment effect. To eliminate those size distortions, we propose a modified t-statistic that uses a null-restricted version of the standard error of the FRD estimator. Simple and asymptotically valid confidence sets for the treatment effect can be also constructed using this null-restricted standard error. An extension to testing for constancy of the regression discontinuity effect across covariates is also discussed. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

We study how to select or combine estimators of the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in the presence of multiple sets of covariates. We consider two cases: (1) all sets of covariates satisfy the unconfoundedness assumption and (2) some sets of covariates violate the unconfoundedness assumption locally. For both cases, we propose a data-driven covariate selection criterion (CSC) to minimize the asymptotic mean squared errors (AMSEs). Based on our CSC, we propose new average estimators of ATE and ATT, which include the selected estimators based on a single set of covariates as a special case. We derive the asymptotic distributions of our new estimators and propose how to construct valid confidence intervals. Our Monte Carlo simulations show that in finite samples, our new average estimators achieve substantial efficiency gains over the estimators based on a single set of covariates. We apply our new estimators to study the impact of inherited control on firm performance. [ABSTRACT FROM PUBLISHER]

Using the 2003 wave of China General Social Survey (2003CGSS), we study the influence of the communist party on individual’s social networks in urban China and, thereby, present a case of socio-political institutions being an important ingredient in social network formation. We adopt a counterfactual framework and estimate the effect of communist party membership on social network investment as an average treatment effect. We find the treatment to be significant. [ABSTRACT FROM PUBLISHER]

In this article, we examine longitudinal data from the Panel Study of Entrepreneurial Dynamics II to infer whether formal business planning affects the amount of formal financing raised by nascent entrepreneurs. Our findings suggest that by and large the use of business plans is mimetic and reveals only limited information about the underlying entrepreneur. Yet, entrepreneurs that write formal business plan receive more formal financial support than had they not planned formally. [ABSTRACT FROM AUTHOR]

This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in multiple periods based on inverse probability weighting. Treatment selection and attrition may depend on both observed characteristics and the unobservable compliance type, which is possibly related to unobserved factors. We also provide a simulation study and apply our methods to the evaluation of a policy intervention targeting college achievement, where we find that controlling for attrition considerably affects the effect estimates. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]