Dongning Ren

Psychologists often rely on observational studies for causal evidence, which raises the question of how to strengthen the design of observational studies for causal inference. One widely used approach in medical research is target trial emulation (TTE). In this tutorial, we introduce TTE to psychological research, with particular emphasis on its cornerstone concept that is routinely ignored in practice: time zero. Following a brief primer on TTE, we foreground time zero and highlight its critical role in improving valid causal inferences from observational data. Using examples drawn from psychological research, we systematically enumerate all possible permutations of the time zero components to show how their misalignment can introduce systematic biases, including selection bias, post-treatment confounding, treatment-dependent (in)eligibility, and immortal time bias, even when all baseline confounders are measured. To address this, we develop a decision framework for integrating time zero into research practices and offer practical recommendations. Through a running example of workplace AI use and employee burnout, we demonstrate how clearly specifying time zero clarifies causal questions, prevents many design-based biases, and strengthens internal validity. We further emphasize that time zero alignment is only one design feature of a strong observational study, and we delineate the boundaries of TTE, cautioning against the misconception that it is a magic fix that automatically delivers causal conclusions. We conclude that explicitly justifying time zero is a necessary step toward improving the rigor and transparency of causal research in psychology.

Generalizability is a perennial concern in randomized studies. While randomized studies are the gold standard for establishing causality, study samples are rarely representative of broader populations due to factors such as convenience sampling, participant self-selection, and researchers' inclusion or exclusion criteria. To strengthen the generalizability of randomized experiments, the framework of causal effect generalizability provides a solution. However, existing methods require accessing representative individual-level data from the target population, which is often unavailable due to limited resources, data access restrictions, or privacy concerns. In this paper, we develop a novel method to generalize causal effects using only summary statistics on covariates from the target population. We illustrate the estimator using a real-world study by generalizing the impact of a climate change behavioral intervention from the study sample to a broader population. By avoiding the need for individual-level data from the target population, our method offers a practical tool for generalizing causal findings from randomized studies. We hope that the proposed method helps build more accurate theories and enhance the policy relevance of behavioral and psychological research.

Can people accurately predict how major political events will affect their views of themselves and the world? We assessed people’s accuracy in forecasting their affective reactions and changes in enduring psychological traits to the 2024 U.S. presidential election. In a three-wave longitudinal study, people made forecasts of their affective states and traits encompassing well-being (life satisfaction, meaning in life), views of the self (self-esteem, perceived control, optimism) and of the world (cynicism, experiences of disrespect, institutional trust) for the event of Democratic and Republican candidate victory twice: 3 weeks before the election (remote forecast) and on the election day (immediate forecast). Following the election results being called, people reported their experiences. Partisan losers overestimated the event impact on their affective reactions but also trait well-being, self-views and worldviews. Partisan winners did not display systematic forecasting errors. The results demonstrate that forecasting errors extend to predictions of changes in enduring traits.

The 2024 U.S. presidential election seemed to have the potential to profoundly impact the national economy, financial markets, and geopolitics. Did Donald Trump’s re-election influence Americans’ psychology as well? We conducted a 7-wave longitudinal survey tracking N = 623 Americans (36% male, M _age = 45.05 years ( SD _age = 22.97) from 3 weeks before the election to 16 weeks after. As the election results came in, Democratic supporters reported a decrease in well-being, optimism and personal control, lower institutional trust, higher cynicism, more experiences of disrespect, and a stronger conspiracy mentality—changes that persisted up to 4 months post-election. In contrast, Republican supporters experienced changes in the opposite direction, effectively reversing the previously observed liberal advantage in institutional trust and diminishing the liberal–conservative gap in other psychological outcomes. These results challenge the notion of inherent psychological differences between liberals and conservatives, highlighting how such differences can shift depending on which party holds power.

Does ostracism disproportionally affect certain groups? Which groups are at greater risk, and in which social domains (e.g., family, workplace)? To address these questions, we adopted a descriptive approach, leveraging a probability-based sample of Dutch adults, with 5,290 participants and 22,075 ratings of ostracism experiences in various social domains. The distribution of ostracism ratings was zero-inflated and extremely skewed: the majority of values clustered at the left end of the spectrum, indicating ostracism as a rare experience; a smaller number of values formed a tail of statistical outliers indicating ostracism as a frequent experience. Low-income (vs. high) earners had over three times higher odds of facing ostracism frequently by family members and people at work. Immigrants had two to three times higher odds of experiencing ostracism frequently than non-immigrants across all domains. These results highlight group-based disparities in ostracism experiences and identify priority populations in need of support.

The field of ostracism research is witnessing a growing interest in understanding ostracism - being excluded and ignored - as a lived experience outside of the laboratory context. How do researchers draw valid causal conclusions about naturally occurring experiences of ostracism without relying on experimental designs? In this article, we draw on insights from the well-established causal inference framework to emphasize a critical step for strengthening causal rigor: stating the causal estimand. Using an intuitive example, we illustrate what a causal estimand is, how to define it, and why it matters. With this article, we encourage readers to think clearly about causal estimands before conducting any data analysis. This conceptual step holds the potential for enhancing the rigor and precision of research studying ostracism as a naturally occurring phenomenon.

Ostracism, feeling ignored and excluded, causes social pain and lowers well-being. People cope with ostracism in various ways. However, past studies on coping with ostracism often focused on one specific behavior (e.g., aggression), measured at a single time point. Such designs limit our understanding of the diversity and temporal trajectory of coping behaviors. We used a time-contingent sampling method with two additional follow-up assessments to capture immediate and later coping responses at Day 0, Day 3, and Day 6 after a naturally occurring ostracism experience ( N = 143; 363 coping responses:). Our findings reveal that individuals initially prioritize withdrawal and prosocial coping, with prosocial responses becoming more prominent over time, while antisocial coping remained rare throughout all assessments. Moreover, individuals exhibited other (cognitive) coping strategies and non-responses, going beyond current ostracism coping taxonomies. These findings advance the understanding of ostracism coping by emphasizing a temporal perspective.

Scholars are often interested in evaluating the causal effects of a recurring exposure (e.g., family violence) on behavioral and psychological outcomes. However, causal inference of recurring exposures is challenging. Conventional analytic approaches target causal quantities lacking practical relevance, such as mandating everyone to uniformly always be exposed or unexposed to family violence. Estimation further relies on everyone having a non-zero probability of being either exposed or unexposed at each occurrence, which is frequently unrealistic when past exposures perfectly predict future exposures. In this paper, we introduce a novel approach from the causal inference literature for drawing causal conclusions about recurring exposures: the incremental propensity score intervention (IPSI). IPSI frames causal questions more realistically by assessing how changing the propensity of a recurring exposure may influence an outcome. To facilitate the adoption of IPSI for recurring exposures, we develop an estimation procedure using lavaan, a widely used structural equation modeling software in R. We demonstrate the application of IPSI with a real-world dataset investigating the impact of recurring family violence on adolescent depression. IPSI requires fewer assumptions than existing approaches while offering more meaningful insights into the causal effects of recurring exposures.

Psychological science holds substantial promise for informing policy decisions but faces challenges in realizing its potential. One widely recognized challenge is bridging the gap between the nonrepresentative study samples commonly used to evaluate interventions and the broader populations that policymakers aim to serve. To address this challenge, we introduce causal effect generalizability, an approach from causal inference and epidemiology, in the form of an accessible, nontechnical tutorial for psychological and behavioral scientists. We use publicly available data from a real-world psychology intervention study to illustrate why causal effects in a nonrepresentative study sample may systematically differ from those in a broader population. We provide a step-by-step guide with user-friendly R functions, enabling researchers to generalize causal effects from a study sample back to the full target population. This approach allows researchers to assess intervention effects in broader populations, offering valuable insights to guide evidence-based policy development. We hope this nontechnical introductory material will assist scholars in enhancing the policy relevance and real-world impact of psychological science.