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There are languages in which the irrealis domain is split up into situations that may potentially occur and counterfactual situations. In these languages, one marker is only used for expressing potential situations (weak irrealis) and another marker is only used for expressing counterfactual situations (strong irrealis). Moreover, there are languages that only have either weak or strong irrealis markers. For languages containing both weak and strong irrealis markers, it has been demonstrated that the use of weak irrealis markers in counterfactual conditionals is blocked by strong irrealis markers. Based on a sample of 51 languages, the present study lends support to this theoretical claim. However, it is also shown that there are other blocking effects. First, there are languages in which the use of strong irrealis markers in counterfactual conditionals is blocked by specialized clause-linking devices (e.g., devices only used for expressing counterfactual conditional relations). Second, for languages that only contain weak irrealis markers, it is shown that the use of weak irrealis markers in counterfactual conditionals is blocked by a specialized clause-linking device. The paper further investigates whether the results obtained for counterfactual conditionals can be generalized to other counterfactual constructions. [ABSTRACT FROM AUTHOR]

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a realtime quantification of the effects of relaxing countermeasures. [ABSTRACT FROM AUTHOR]

SYNOPSIS: We conduct an experiment with practicing Big 4 audit seniors and demonstrate that generating counterexplanations for an event in an unrelated task prior to completing audit planning tasks activates a counterfactual mindset that enhances auditors' professional skepticism. This approach to activating professional skepticism can be implemented without auditor training and can be deployed across many different audit tasks. Overall, the experiment indicates that activation of a counterfactual mindset has significant potential to enhance audit quality. [ABSTRACT FROM AUTHOR]

While there have been some studies of counterfactual analysis in history and other academic disciplines, there are very few studies in social policy and health policy. This paper explores a major critical juncture and counterfactual in the creation of the NHS. In particular, it explores the critical juncture of the discussion in the Labour Cabinet involving Bevan's proposal for nationalising the hospitals and Morrison's alternative proposal based on local government, and the counterfactual of Prime Minister Attlee summing up in favour of Morrison. It reviews the literature on the criteria for counterfactuals, and justifies the focus on the Cabinet Discussion, as this was a considered option which, with minimum change, might have led to significantly different outcomes. We consider how such an event could have come about, how credible that alternative was, and what its implications might have been. [ABSTRACT FROM AUTHOR]

In multi-task learning, a learner adds the learned programs into background knowledge (BK) and reuses them to learn other programs. Continually acquiring BK can lead to the problem of excessive BK, which overwhelms a learning system. Hence, it is necessary to forget irrelevant BK. However, existing forgetting approaches rarely consider the relevance between BK and learning tasks, commonly providing the same BK for different induction tasks. To address this issue, this paper proposes a relevance identification approach based on counterfactual thinking, termed counterfactual acquisition. This approach first measures each hypothesis' s contribution to the learning task using a relevance function. Then, it retains only those hypotheses whose relevance function values exceed a predefined threshold. Moreover, this approach is applied to inductive logic programming (ILP) through the introduction of a multi- task ILP learner named Countergol. Theoretical analysis demonstrates that Countergol can reduce the hypothesis space and sample complexity size. Experimental comparisons against other forgetting approaches show that Countergol outperforms similar methods. [ABSTRACT FROM AUTHOR]

Though integral to thinking about causality, narrative--in particular narrative thinking--has been overlooked in traditional approaches to creativity studies and creative problem solving. Emphasizing the intrinsically speculative and causal aspects of narrative, this study investigates the effectiveness of narrative creativity training as a means of fostering innovative problem-solving skills within the US Army. Through two experiments conducted at the US Army Command and General Staff College, this research compares the outcomes of narrative training against conventional associational creativity training. Our results suggest that specifically narrative capacities are cognitively distinct, can be directly trained, and substantially improve problem solving. This research contributes to scholarship on the practical and empirical applications of narrative theory, as well as scholarship in creativity studies and creative problem solving, evincing the specific utility of narrative thinking in addressing complex, open-ended challenges. [ABSTRACT FROM AUTHOR]

The abductive natural language inference task (Abductive NLI) seeks to select more plausible hypothetical events based on given antecedent events and consequent events. However, inherent biases such as “logical defects” and “single-sentence label leakage” stemming from mediator and confounding variables in the inference process pose challenge. To address these issues, this paper proposes a novel knowledge-aware debiased inference model integrating intervention and counterfactual (KDIC). The model comprises three key modules: the mediator modulation module, the hypothesis-only bias module, and the external knowledge fusion module. Firstly, the mediator modulation module consists of causal graph intervention and hypothesis contrast learning. Causal graph intervention constructs a potential causal graph from given events and then extracts mediator variables, standing for the potential feature of unobserved events, via self-attention mechanism and graph convolutional network for guiding deep encoding. Concurrently, hypothesis contrast learning encourages the model to discern key factors affecting hypothesis judgment, rectifying logical inconsistencies. Secondly, the hypothesis-only bias module addresses the counterfactual problem by proactively identifying the inference biases arising from “single-sentence label leakage”. This module reduces the model’s reliance on specific words or phrases in the hypothesis, thereby enhancing robustness. Finally, this paper leverages a pre-trained common sense knowledge graph encoder, ComET, within the external knowledge fusion module. This integration enriches the model’s understanding of observed events’ motivations and potential outcomes, bolstering logical coherence across events. Experiments results on the αNLI dataset demonstrate that the inference ability of KDIC is second only to Electra-large-discriminator trained via self-supervised learning. However, KDIC exhibits greater robustness to alleviate biases in the inference process.

Visual question answering (VQA) is a pivotal topic at the intersection of computer vision and natural language processing. This paper addresses the challenges of linguistic bias and bias fusion within invalid regions encountered in existing VQA models due to insufficient representation of multi-modal features. To overcome those issues, we propose a multi-feature enhancement scheme. This scheme involves the fusion of one or more features with the original ones, incorporating discrete cosine transform (DCT) features into the counterfactual reasoning framework. This approach harnesses finegrained information and spatial relationships within images and questions, enabling a more refined understanding of the indirect relationship between images and questions. Consequently, it effectively mitigates linguistic bias and bias fusion within invalid regions in the model. Extensive experiments are conducted on multiple datasets, including VQA2 and VQA-CP2, employing various baseline models and fusion techniques, resulting in promising and robust performance.

Counterfactuals are alternative outcomes to past events. Curiosity for the counterfactual acts as an important driver of learning under uncertainty and helps to improve on past behaviors by informing future choices. Humans have demonstrated the desire to collect counterfactual information when it is perceived as useful, during times of uncertainty, and even when it comes at a cost. The comparative literature on counterfactual use and curiosity is small but suggests that rhesus macaques may possess similar tendencies to humans. The goal of the current study was to add a new species, capuchin monkeys (Sapajus apella), to the comparative counterfactual literature and to test capuchins and humans on nearly identical tasks. This study involved a three-choice gambling task that consisted of one hidden and two visible reward values. Visible reward values were always equivalent but had differently colored borders to indicate whether participants would view the counterfactual information associated with the gamble or not on trials when they chose not to gamble. Only humans showed evidence of counterfactual curiosity and collected the information at levels different than chance. Humans also collected counterfactual information significantly more than capuchins, indicating a species difference. However, it is not clear whether this difference is simply due to the capuchins’ lack of curiosity surrounding this particular task or whether there is a broader species difference for seeking counterfactuals resulting from different evolutionary histories of these species.

Abstract Background If US adults who smoke cigarettes are switching to e-cigarettes, the effect may be observable at the population level: smoking prevalence should decline as e-cigarette prevalence increases, especially in sub-populations with highest e-cigarette use. This study aimed to assess such effects in recent nationally-representative data. Methods We updated a prior analysis with the latest available National Health Interview Survey data through 2022. Data were cross-sectional estimates of the yearly prevalence of smoking and e-cigarette use, respectively, among US adults and among specific age, race/ethnicity, and sex subpopulations. Non-linear models were fitted to observed smoking prevalence in the pre-e-cigarette era, with a range of ‘cut-off’ years explored (i.e., between when e-cigarettes were first introduced to when they became widely available). These trends were projected forward to predict what smoking prevalence would have been if pre-e-cigarette era trends had continued uninterrupted. The difference between actual and predicted smoking prevalence (‘discrepancy’) was compared to e-cigarette use prevalence in each year in the e-cigarette era to investigate whether the observed decline in smoking was statistically associated with e-cigarette use. Results Observed smoking prevalence in the e-cigarette era was significantly lower than expected based on pre-e-cigarette era trends; these discrepancies in smoking prevalence grew as e-cigarette use prevalence increased, and were larger in subpopulations with higher e-cigarette use, especially younger adults aged 18–34. Results were robust to sensitivity tests varying the analysis design. Conclusions Population-level data continue to suggest that smoking prevalence has declined at an accelerated rate in the last decade in ways correlated with increased uptake of e-cigarette use.

The time it takes for an ecosystem to recover from a disturbance is a key to environmental management. Conventionally, recovery is defined as a return to the pre‐disturbance state, assuming ecosystem stationarity. However, this view does not account for the impact of external forces like climate change, imposing non‐stationarity and trends. Alternatively, the counterfactual approach views recovery as the state the ecosystem would have achieved if the disturbance had not occurred, accounting for external forces. Here, we present a simple method to estimate the counterfactual recovery time. By implementing our method to the greening of the Arctic region, we showed that counterfactual greening recovery is twice as long as conventional recovery over the region. We argue that the well‐documented greening of the region acts as an external force, leading to such a large difference. We advocate for embracing the counterfactual definition of recovery, as it aligns with realistic decision‐making processes. Plain Language Summary: The time required for an ecosystem to recover from a disturbance is a crucial factor in many areas, such as decision‐making. Traditionally, this recovery time has been considered as the period for a disturbed ecosystem to either bounce back to its pre‐disturbance state or reach a new stable state. However, the assumption of stability may not hold true in light of climate change and other external factors. These forces make ecosystems dynamic, potentially with upward or downward trends, rather than reaching a stable state. To account for these external forces, an alternative definition is presented: the time for an ecosystem to reach its potential state, as if the disturbance had not occurred, termed the "counterfactual recovery." In this study, we propose a simple method based on time series analyses to calculate the counterfactual recovery time. We showcase the difference between the two definitions by implementing our approach to the case of greening in the Arctic and boreal regions post‐disturbance. Our method is generalizable to any ecological system, and we envision its contribution to decision‐making processes, such as conservation efforts. Key Points: The conventional definition to recovery as return to pre‐disturbance state ignores the impact of external forces on the recovery processWe proposed a method to estimate the time to recover using counterfactual approach which considers the impact of external forcesImplementing our approach over the greening of the arctic region showed the large difference between the two approaches [ABSTRACT FROM AUTHOR]

Using a novel geospatial panel combined with data from the 2015 American Community Survey (ACS), we investigate the effect of topography—altitude and terrain unevenness—on income segregation at the neighborhood level. Specifically, we perform large‐scale counterfactual simulations by estimating household preferences for topography, altering the topographical profile of each city, and observing the resulting neighborhood sorting outcome. We find that unevenness contributes to the segmentation of markets: in the absence of hilliness, rich and poor households experience greater mixing. Hillier cities are more income‐segregated because of their unevenness; the opposite is true for flatter cities. [ABSTRACT FROM AUTHOR]

Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating synthetic samples from an original sample to achieve the desired outcome in a machine learning model, thus helping to understand the prediction. An insightful methodology would explore a broader set of counterfactual antecedents to reveal multiple possibilities while operating on any classifier. Therefore, we create a tree-based search that requires monotonicity from the objective functions (a.k.a. cost functions); it allows pruning branches that will not improve the objective functions. Since monotonicity is only required for the objective function, this method can be used for any family of classifiers (e.g., linear models, neural networks, and decision trees). However, additional classifier properties speed up the tree search when it foresees branches that will not result in feasible actions. Moreover, the proposed optimization generates a diverse set of Pareto-optimal counterfactual antecedents by relying on multi-objective concepts. The results show an algorithm with working guarantees that enumerates a wide range of counterfactual antecedents. It helps the decision-maker understand the machine learning decision and finds alternatives to achieve the desired outcome. The user can inspect these multiple counterfactual antecedents to find the most suitable one and better understand the prediction. [ABSTRACT FROM AUTHOR]

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In the spirit of this exchange's call for more "methodological talk" in explanation, I suggest that polyphonic disunity that has prompted persistent calls in economic geography for some form of unificatory explanation may be pursued through opportunities in counterfactual and consilience explanations. These opportunities acknowledge a unificatory continuum that does not eschew explanatory autonomy while drawing multivoices and their methodologies into proximity. [ABSTRACT FROM AUTHOR]

Background: If US adults who smoke cigarettes are switching to e-cigarettes, the effect may be observable at the population level: smoking prevalence should decline as e-cigarette prevalence increases, especially in sub-populations with highest e-cigarette use. This study aimed to assess such effects in recent nationally-representative data. Methods: We updated a prior analysis with the latest available National Health Interview Survey data through 2022. Data were cross-sectional estimates of the yearly prevalence of smoking and e-cigarette use, respectively, among US adults and among specific age, race/ethnicity, and sex subpopulations. Non-linear models were fitted to observed smoking prevalence in the pre-e-cigarette era, with a range of 'cut-off' years explored (i.e., between when e-cigarettes were first introduced to when they became widely available). These trends were projected forward to predict what smoking prevalence would have been if pre-e-cigarette era trends had continued uninterrupted. The difference between actual and predicted smoking prevalence ('discrepancy') was compared to e-cigarette use prevalence in each year in the e-cigarette era to investigate whether the observed decline in smoking was statistically associated with e-cigarette use. Results: Observed smoking prevalence in the e-cigarette era was significantly lower than expected based on pre-e-cigarette era trends; these discrepancies in smoking prevalence grew as e-cigarette use prevalence increased, and were larger in subpopulations with higher e-cigarette use, especially younger adults aged 18–34. Results were robust to sensitivity tests varying the analysis design. Conclusions: Population-level data continue to suggest that smoking prevalence has declined at an accelerated rate in the last decade in ways correlated with increased uptake of e-cigarette use. [ABSTRACT FROM AUTHOR]

The book contains an extraordinary condensation of important themes regarding populism. It brings social and political science together with normative philosophy, something badly needed today in critical theory to advance its theoretical-empirical approach. But it is precisely the kind of interpretation of critical theory presented in the book that is the focus of these brief comments. In particular, I mainly ask about the relation to second-generation critical theory. In this context, the comments particularly address kinds and levels of cultural structure that make possible the form of normative reconstruction offered by critical theory, and examines how these appear – or do not appear – in the book, together with outlining the implications for the approach. Specifically, I will address three main dimensions arising in the book. These are (a) Immanent Critique; (b) The counterfactual status of ideals; (c) The methodology of ideal type analysis. The general conclusion is show in what ways the line of inquiry present in the book can be further elaborated within critical theory. [ABSTRACT FROM AUTHOR]

The article introduces FairGap, a novel approach for fairness-aware recommendation systems, which directly intervenes in observed interactions to generate a counterfactual graph, enabling fair representations for users and items. Topics include the challenges of biased user-item interactions, the FairGap framework for generating counterfactual graphs, and its effectiveness in improving recommendation fairness through theoretical proofs and experimental validation.

Visual question answering (VQA) is a pivotal topic at the intersection of computer vision and natural language processing. This paper addresses the challenges of linguistic bias and bias fusion within invalid regions encountered in existing VQA models due to insufficient representation of multi-modal features. To overcome those issues, we propose a multi-feature enhancement scheme. This scheme involves the fusion of one or more features with the original ones, incorporating discrete cosine transform (DCT) features into the counterfactual reasoning framework. This approach harnesses finegrained information and spatial relationships within images and questions, enabling a more refined understanding of the indirect relationship between images and questions. Consequently, it effectively mitigates linguistic bias and bias fusion within invalid regions in the model. Extensive experiments are conducted on multiple datasets, including VQA2 and VQA-CP2, employing various baseline models and fusion techniques, resulting in promising and robust performance. [ABSTRACT FROM AUTHOR]

Investigation of health inequities tend to be examined, in human neurosciences, as biological factors at the level of the individual. In actuality, health inequities arise, due largely in part, to deep-seated structural factors. Structural inequality refers to the systemic disadvantage of one social group compared to others with whom they coexist. The term encompasses policy, law, governance, and culture and relates to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These structural inequalities include but are not limited to social segregation, the intergenerational effects of colonialism and the consequent distribution of power and privilege. Principles to address inequities influenced by structural factors are increasingly prevalent in a subfield of the neurosciences, i.e., cultural neurosciences. Cultural neuroscience articulates the bidirectional relationship between biology and environmental contextual factors surrounding research participants. However, the operationalization of these principles may not have the intended spillover effect on the majority of human neurosciences: this limitation is the overarching focus of the present piece. Here, we provide our perspective that these principles are missing and very much needed in all human neuroscience subdisciplines to accelerate our understanding of the human brain. Furthermore, we provide an outline of two key tenets of a health equity lens necessary for achieving research equity in human neurosciences: the social determinants of health (SDoH) framework and how to deal with confounders using counterfactual thinking. We argue that these tenets should be prioritized across future human neuroscience research more generally, and doing so is a pathway to further gain an understanding of contextual background intertwined with the human brain, thus improving the rigor and inclusivity of human neuroscience research.

We evaluated the impact of a philanthropic program investing in the conservation of sites along the Pacific Americas Flyway, which spans >16,000 km of coastline and is used by millions of shorebirds. Using a quasi‐experimental, mixed methods approach, we estimated what would have happened to shorebird populations at 17 wintering sites without the sustained and additional investment they received. We modeled shorebird populations across the entire flyway and at sites with and without investment. Combining shorebird abundance estimates with a land‐cover classification model, we used the synthetic control method to create counterfactuals for shorebird trends at the treatment sites. We found no evidence of an overall effect across three outcome variables. Species‐ and site‐level treatment effects were heterogeneous, with a few cases showing evidence of a positive effect, including a site with a high level of overall investment. Results suggest six shorebirds declined across the entire flyway, including at many Latin American sites. However, the percentage of flyway populations present at the sites remained stable, and the percentage at the treatment sites was higher (i.e., investment sites) than at control sites. Multiple mechanisms behind our results are possible, including that investments have yet to mitigate impacts and negative impacts at other sites are driving declines at the treatment sites. A limitation of our evaluation is the sole focus on shorebird abundance and the lack of data that prohibits the inclusion of other outcome variables. Monitoring infrastructure is now in place to design a more robust and a priori shorebird evaluation framework across the entire flyway. With this framework, it will prove easier to prioritize limited dollars to result in the most positive conservation outcomes. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) are central to sustainability targets, yet few evaluations explore outcomes for both conservation and development, or the trade‐offs involved. We applied counterfactual analyses to assess the extent to which PAs maintained forest cover and influenced well‐being across >31,000 villages in Sumatra and Kalimantan, Indonesia. We examined multidimensional aspects of well‐being, tracking education, health, living standards, infrastructure, environment, and social cohesion in treatment and control villages between 2005 and 2018. Overall, PAs were effective at maintaining forest cover compared to matched controls and were not detrimental to well‐being. However, impacts were highly heterogeneous, varying by island and strictness of protection. While health, living standards, and infrastructure aspects of well‐being improved, education access, environmental conditions, and social cohesion declined. Our analysis reveals the contexts through which individual PAs succeed or fail in delivering multiple benefits and provides insights into where further on‐ground support is needed to achieve conservation and development objectives. [ABSTRACT FROM AUTHOR]

Abstract The time it takes for an ecosystem to recover from a disturbance is a key to environmental management. Conventionally, recovery is defined as a return to the pre‐disturbance state, assuming ecosystem stationarity. However, this view does not account for the impact of external forces like climate change, imposing non‐stationarity and trends. Alternatively, the counterfactual approach views recovery as the state the ecosystem would have achieved if the disturbance had not occurred, accounting for external forces. Here, we present a simple method to estimate the counterfactual recovery time. By implementing our method to the greening of the Arctic region, we showed that counterfactual greening recovery is twice as long as conventional recovery over the region. We argue that the well‐documented greening of the region acts as an external force, leading to such a large difference. We advocate for embracing the counterfactual definition of recovery, as it aligns with realistic decision‐making processes.

Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved. [ABSTRACT FROM AUTHOR]

Estimating treatment (or policy or intervention) effects on a single individual or unit has become increasingly important in health and biomedical sciences. One method to estimate these effects is the synthetic control method, which constructs a synthetic control, a weighted average of control units that best matches the treated unit's pre-treatment outcomes and other relevant covariates. The intervention's impact is then estimated by comparing the post-intervention outcomes of the treated unit and its synthetic control, which serves as a proxy for the counterfactual outcome had the treated unit not experienced the intervention. The augmented synthetic control method, a recent adaptation of the synthetic control method, relaxes some of the synthetic control method's assumptions for broader applicability. While synthetic controls have been used in a variety of fields, their use in public health and biomedical research is more recent, and newer methods such as the augmented synthetic control method are underutilized. This paper briefly describes the synthetic control method and its application, explains the augmented synthetic control method and its differences from the synthetic control method, and estimates the effects of an antimalarial initiative in Mozambique using both the synthetic control method and the augmented synthetic control method to highlight the advantages of using the augmented synthetic control method to analyze the impact of interventions implemented in a single region. [ABSTRACT FROM AUTHOR]

The type of mood or tense marking that causes counterfactuality inferences—as figuring prominently, but far from exclusively, in counterfactual conditionals—has not yet received a comprehensive and compositional analysis. Focusing on four languages, the paper presents under-appreciated facts and a novel theory where the mood serves to activate alternatives to modal operators, particularly one: the identity operator, often giving rise to counterfactual implicatures. [ABSTRACT FROM AUTHOR]

The physical climate storyline (PCS) approach is increasingly recognized by the physical climate research community as a tool to produce and communicate decision-relevant climate risk information. While PCS is generally understood as a single concept, different varieties of the approach are applied according to the aims and purposes of the PCS and the scientists that build them. To unpack this diversity of detail, this article gives an overview of key practices and assumptions of the PCS approach as developed by physical climate scientists, as well as their ties to similar approaches developed by the broader climate risk and adaptation research community. We first examine varieties of PCSs according to the length of the causal chain they explore, and the type of evidence used. We then describe how they incorporate counterfactual elements and the temporal perspective. Finally, we examine how value judgments are implicitly or explicitly included in the aims and construction of PCSs. We conclude the discussion by suggesting that the PCS approach can further mature in the way it incorporates the narrative element, in the way it incorporates value judgments, and in the way that the evidence chosen to build PCSs constrains what is considered plausible. [ABSTRACT FROM AUTHOR]

Measuring the immediate impact of television advertisements (TV ads) on online traffic poses significant challenges in many aspects. Nonetheless, a comprehensive consideration is essential to fully grasp consumer reactions to TV ads. So far, the measurement of this effect has not been studied to a large extent. Existing studies have either determined how a specific focus group, i.e., toddlers, people of a certain age group, etc., react to ads via simple statistical tests using a case study approach or have examined the effects of advertising with simple regression models. This study introduces a comprehensive framework called TV-Impact. The framework uses a Bayesian structural time-series model called CausalImpact. There are additional novel approaches developed within the framework. One of the novelties of TV-Impact lies in its dynamic algorithm for selecting control variables which are supporting data sources and presumed to be unaffected by TV ads. In addition, we proposed the concept of Group Ads to combine overlapping ads into a single ad structure. Then, Random Forest Regressor, which is a commonly preferred supervised learning method, is used to decompose the impact into single ads. The TV-Impact framework was applied to the data of iLab, a venture company in Turkey, and manages its companies' advertising strategies. The findings reveal that the TV-Impact model positively influenced the companies' strategies for allocating their TV advertisement budgets and increased the amount of traffic driven to company websites, serving as an effective decision support system. [ABSTRACT FROM AUTHOR]

People can think about hypothetical impossibilities and a curious observation is that some impossible conditionals seem true and others do not. Four experiments test the proposal that people think about impossibilities just as they do possibilities, by attempting to construct a consistent simulation of the impossible conjecture with its suggested outcome, informed by their knowledge of the real world. The results show that participants judge some impossible conditionals true with one outcome, for example, "if people were made of steel, they would not bruise easily" and false with the opposite outcome, "if people were made of steel they would bruise easily", and others false with either outcome, for example, "if houses were made of spaghetti, their engines would (not) be noisy". However, they can sometimes judge impossible conditionals true with either outcome, for example, "if Plato were identical to Socrates, he would (not) have a small nose", or "if sheep and wolves were alike, they would (not) eat grass". The results were observed for judgments about what could be true (Experiments 1 and 4), judgments of degrees of truth (Experiment 2), and judgments of what is true (Experiment 3). The results rule out the idea that people evaluate the truth of a hypothetical impossibility by relying on cognitive processes that compare the probability of each conditional to its counterpart with the opposite outcome. [ABSTRACT FROM AUTHOR]

COVID-19 compelled the implementation of prolonged confinements to limit human contact, and the consequences were devastating for the tourism industry. This study estimates the direct and indirect effects of COVID-19 on tourism in Mexico by constructing three counterfactuals of international tourists and their average expenditure using time series models and the pre-pandemic expectations of economic analysts. Econometric univariate and auto-regressive distributed lag models were employed to estimate the direct impact of COVID-19 on the sector, and input-output multipliers were utilized to assess the indirect and total effects of the shock on the economy. Estimates indicate that the impact of COVID-19 from March to December 2020 caused a loss of approximately US$15 billion in international tourism revenues, indicating a 15% decline in tourism gross domestic product (GDP) and the loss of one-fifth of the tourism workforce. In 2020, international tourism alone could have contributed one-eighth to the Mexican economy's collapse. [ABSTRACT FROM AUTHOR]

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

Abstract Protected areas (PAs) are central to sustainability targets, yet few evaluations explore outcomes for both conservation and development, or the trade‐offs involved. We applied counterfactual analyses to assess the extent to which PAs maintained forest cover and influenced well‐being across >31,000 villages in Sumatra and Kalimantan, Indonesia. We examined multidimensional aspects of well‐being, tracking education, health, living standards, infrastructure, environment, and social cohesion in treatment and control villages between 2005 and 2018. Overall, PAs were effective at maintaining forest cover compared to matched controls and were not detrimental to well‐being. However, impacts were highly heterogeneous, varying by island and strictness of protection. While health, living standards, and infrastructure aspects of well‐being improved, education access, environmental conditions, and social cohesion declined. Our analysis reveals the contexts through which individual PAs succeed or fail in delivering multiple benefits and provides insights into where further on‐ground support is needed to achieve conservation and development objectives.

Abstract Background Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed “harvesting” in the literature. Methods We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a “ball and urn” model. Results Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy. Conclusion Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.