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Upward counterfactual thinking involves imagining favourable situations that could have changed the outcome of a negative event. Although it has been reliably positively associated with depression, a causal relationship has not yet been investigated. This study addressed this gap in the literature by examining whether upward counterfactual thinking causally increases state depression. The online experimental study was conducted on 469 Philippine residents ( M age = 29.45; SD  = 10.35; Range 18–72). As predicted, individuals who were induced to engage in an upward counterfactual thinking writing activity regarding a previous negative experience related to an unattained goal reported higher state depression relative to individuals who completed a neutral writing task. Consistent with the sequential negative cognitions-to-affect framework articulated by theories of depression, regret mediated the link between upward counterfactual thinking and depression. Contrary to expectation, induced upward counterfactual thinking increased state depression when perceived personal control over the negative experience was low or moderate but not when high. Future opportunity to change the negative experience was independently associated with decreased state depression but did not interact with upward counterfactual thinking to influence responses. Implications of these findings are discussed.

In this paper, the roles of upward counterfactual thinking and their perception of COVID-19 risk in the influence of social comparison tendency on travel satisfaction was investigated. The study follows the design of 3 (social comparison tendency: high, low and medium) by 2 (perceived risk degree of COVID-19: high and low). The findings are as follows: (1) There are significant differences in the tourism satisfaction of individuals with different social comparison tendencies: the tourism satisfaction of individuals with low social comparison tendencies is significantly higher than that of the other two groups, and the tourism satisfaction of individuals with high social comparison tendencies is higher than that of individuals with medium social comparison tendencies; (2) The influence of social comparison tendency on tourism satisfaction is mediated by upward counterfactual thinking and moderated by the perception of COVID-19 risk. These findings help the tourism industry to better understand consumers' psychological needs and behavior patterns, so as to design tourism products and marketing strategies that better meet consumers' expectations. Especially after the epidemic, understanding consumers' risk perception and its impact on satisfaction can help the tourism industry to cope with the potential health crisis more effectively, improving the quality of tourism services and enhance consumers' confidence.

This paper introduces a simple framework of counterfactual estimation for causal inference with time-series cross-sectional data, in which we estimate the average treatment effect on the treated by directly imputing counterfactual outcomes for treated observations. We discuss several novel estimators under this framework, including the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator and matrix completion estimator. They provide more reliable causal estimates than conventional two-way fixed effects models when treatment effects are heterogeneous or unobserved time-varying confounders exist. Moreover, we propose a new dynamic treatment effects plot, along with several diagnostic tests, to help researchers gauge the validity of the identifying assumptions. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Comparative thinking is ubiquitous in human cognition. Empirical evidence is accumulating that PTSD symptomatology is linked to various changes in social, temporal and counterfactual comparative thinking. However, no systematic review and meta-analysis in this line of research have been conducted to this date. We searched titles, abstracts and subject terms of electronic records in PsycInfo and Medline from inception to January 2019 with various search terms for social, temporal and counterfactual comparative thinking as well as PTSD. Journal articles were included if they reported a quantitative association between PTSD and social, temporal and/or counterfactual comparative thinking in trauma-exposed clinical or sub-clinical samples. A total of 36 publications were included in the qualitative synthesis. The number of publications on the association between PTSD and social and temporal comparative thinking was too scarce to warrant a meta-analytic review. A narrative review of available literature suggests that PTSD is associated with distortions in social and temporal comparative thinking. A meta-analysis of 24 independent samples (n = 4423) assessing the association between PTSD and the frequency of counterfactual comparative thinking yielded a medium to large positive association of r =.464 (p <.001, 95% CI =.404; .520). Higher study quality was associated with higher magnitude of association in a meta-regression. Most studies collected data cross-sectionally, precluding conclusions regarding causality. Overall, study quality was found to be moderate. More longitudinal and experimental research with validated comparative thinking measures in clinical samples is needed to acquire a more sophisticated understanding of the role of comparative cognitions in the aetiology and maintenance of PTSD. Comparative thinking might be a fruitful avenue for a better understanding of posttraumatic reactions and improving treatment.

Our tendency to engage in episodic counterfactual thinking—namely, imagining alternative ways in which past personal events could have occurred but did not—is ubiquitous. Although widely studied by cognitive and social psychologists, this autobiographically based variety of counterfactual thought has been connected only recently to research on the cognitive and neuroscientific basis of episodic memory and mental simulation. In the current article, we offer an empirical characterization of episodic counterfactual thinking by contrasting it with related varieties of mental simulation along three dimensions: temporal context, degree of episodic detail, and modal profile (i.e., perceived possibility or impossibility). In so doing, we offer a practical strategy to navigate the nascent literature on episodic counterfactual thinking within the context of other mental simulations, and we argue that the evidence surveyed strongly indicates that although connected along the aforementioned dimensions, episodic counterfactual thinking is a psychological process different from episodic memory, episodic future thinking, and semantic counterfactual thinking.

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants' eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants' looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios, we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States. As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.

Researchers often seek to identify the effects of a treatment on a sequence of behaviors, such as whether citizens register to vote and whether they then cast ballots. I show that average treatment effects (ATEs) are only identified until the first behavior (registering to vote) that affects the set of possible subsequent actions (voting). When one action changes the set of possible subsequent actions, it creates ‘phantom counterfactuals,’or undefined potential outcomes, which render ATEs unidentified. I show that applied theory allows researchers to diagnose phantom counterfactuals, which helps to recognize unidentified ATEs and focus instead on other estimands that are identified. I illustrate this approach using a stylized model of crime reporting, showing how different theories generate different sets of identified estimands while holding constant an experimental design. I thereby establish the necessity of applied theory for causal identification in empirical research with sequential behavioral outcomes.