Search

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster‐level effects model, leading to a variance penalty that incorporates the signal‐to‐noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra‐class correlation. [ABSTRACT FROM AUTHOR]

We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity‐based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias‐corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines, but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect CD4 guidelines for anti‐retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age‐based patient cost‐sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available data and statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis. [ABSTRACT FROM AUTHOR]

The article focuses on the use of instrumental variables (IVs) in outcomes research. Topics discussed the variables highlight a number of ways to improve the quality of research that based on IV design, mentions the discussion on IV methods that are consistent with the results from randomized trials, and information on the empirical evidence for the IV design.

In some observational studies of treatment effects, matched samples are created so treated and control groups are similar in terms of observable covariates. Traditionally, such matched samples consist of matched pairs. However, alternative forms of matching may have desirable features. One strategy that may improve efficiency is to match a variable number of control units to each treated unit. Another strategy to improve balance is to adopt a fine balance constraint. Under a fine balance constraint, a nominal covariate is exactly balanced, but it does not require individually matched treated and control subjects for this variable. Here, we propose a method to allow for fine balance constraints when each treated unit is matched to a variable number of control units, which is not currently possible using existing matching network flow algorithms. Our approach uses the entire number to first determine the optimal number of controls for each treated unit. For each stratum of matched treated units, we can then apply a fine balance constraint. We then demonstrate that a matched sample for the evaluation of the Peer Health Exchange, an intervention in schools designed to decrease risky health behaviors among youths, using a variable number of controls and fine balance constraint is superior to simply using a variable-ratio match. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medical sciences in recent years. The framework enables us to show exactly what assumptions are sufficient for identifying causal mediation effects for the mechanisms of interest, derive a general algorithm for estimating such mechanism-specific effects, and formulate a sensitivity analysis for the violation of those identification assumptions. We also discuss an extension of the framework to analyze causal mechanisms in the presence of treatment noncompliance, a common problem in randomized evaluation studies. The methods are illustrated via applications to two intervention studies on pre-school classes and job-training workshops. [ABSTRACT FROM AUTHOR]

Scholars of state politics are often interested in the causal effects of legislative institutions on policy outcomes. For example, during the 1990s a number of states adopted term limits for state legislators. Advocates of term limits argued that this institutional reform would alter state policy in a number of ways, including limiting state expenditures. We highlight a number of research design issues that complicate attempts to estimate the effect of institutions on state outcomes by addressing the question of term limits and spending. In particular, we focus on (1) treatment effect heterogeneity and (2) the suitability of nonterm-limit states as good counterfactuals for term-limit states. We compare two different identification strategies to deal with these issues: differences-in-differences ( DID) estimation and conditioning on prior outcomes with an emphasis on synthetic case control. Using more rigorous methods of causal inference, we find little evidence that term limits affect state spending. Our analysis and results are informative for researchers seeking to assess the causal effects of state-level institutions. [ABSTRACT FROM AUTHOR]

When will people become ambivalent about politics? One possibility is that the roots of ambivalence lie within the individual, with differences in political knowledge and attitude strength predicting whether a person internalizes the conflicts of politics. Alternately, attitudinal ambivalence could result from structural differences in the way political choices are presented in the wider political environment. We explore the degree to which different environments promote or limit ambivalence using a matching approach in conjunction with a set of multilevel models. We find that campaign environments can induce candidate ambivalence. In presidential elections, campaign efforts promote ambivalence most when competition between partisan campaign efforts is high. In House elections, campaign spending has a direct effect on levels of candidate ambivalence, where a candidate's spending decreases ambivalence about that candidate and increases ambivalence about opponents. [ABSTRACT FROM AUTHOR]

Dramatic world change has stimulated interest in research questions about the dynamics of politics. We have seen increases in the number of time series data sets and the length of typical time series. But three shortcomings are prevalent in published time series analysis. First, analysts often estimate models without testing restrictions implied by their specification. Second, researchers link the theoretical concept of equilibrium with cointegration and error correction models. Third, analysts often do a poor job of interpreting results. The consequences include weak connections between theory and tests, biased estimates, and incorrect inferences. We outline techniques for estimating linear dynamic regressions with stationary data and weakly exogenous regressors. We recommend analysts (1) start with general dynamic models and test restrictions before adopting a particular specification and (2) use the wide array of information available from dynamic specifications. We illustrate this strategy with data on Congressional approval and tax rates across OECD countries. [ABSTRACT FROM AUTHOR]

It is well understood that trust in government responds to the performance of the president, Congress, and the economy. Despite improved government performance, however, trust has never returned to the levels witnessed in the 1950s and 1960s. Social capital may be the force that has kept trust low. If so, we need to assess the relative contributions of both government performance and social capital at the macro level. Using macrolevel data, the analysis, here, is designed to capture the variation over time in both social capital and government performance and let them compete to explain the macro variation in trust. The empirical results demonstrate that both government performance and social capital matter, but that social capital appears to be the force which accounts for the decline in trust over the last 40 years. [ABSTRACT FROM AUTHOR]