Synthetic controls (SCs) are widely used to estimate the causal effect of a treatment. However, they do not account for the different speeds at which units respond to changes. Reactions may be inelastic or “sticky” and thus slower due to varying regulatory, institutional, or political environments. We show that these different reaction speeds can lead to biased estimates of causal effects. We therefore introduce a dynamic SC approach that accommodates varying speeds in time series, resulting in improved SC estimates. We apply our method to re-estimate the effects of terrorism on income (Abadie and Gardeazabal [2003, American Economic Review 93, 113–132]), tobacco laws on consumption (Abadie, Diamond, and Hainmueller [2010, Journal of the American Statistical Association 105, 493–505]), and German reunification on GDP (Abadie, Diamond, and Hainmueller [2015, American Journal of Political Science 59, 495–510]). We also assess the method’s performance using Monte Carlo simulations. We find that it reduces errors in the estimates of true treatment effects by up to 70% compared to traditional SCs, improving our ability to make robust inferences. An open-source R package, dsc, is made available for easy implementation.

The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than ‘Western, educated, industrialised, rich, and democratic’ (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or ‘selection restriction’), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.

Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

Confounding bias arises when a treatment and outcome share a common cause. In randomised controlled experiments (trials), treatment assignment is random, ostensibly eliminating confounding bias. Here, we use causal directed acyclic graphs to unveil eight structural sources of bias that nevertheless persist in these trials. This analysis highlights the crucial role of causal inference methods in the design and analysis of experiments, ensuring the validity of conclusions drawn from experimental data.

Inverse probability weighting is a common remedy for missing data issues, notably in causal inference. Despite its prevalence, practical application is prone to bias from propensity score model misspecification. Recently proposed methods try to rectify this by balancing some moments of covariates between the target and weighted groups. Yet, bias persists without knowledge of the true outcome model. Drawing inspiration from the quasi maximum likelihood estimation with misspecified statistical models, I propose an estimation method minimizing a distance between true and estimated weights with possibly misspecified models. This novel approach mitigates bias and controls mean squared error by minimizing their upper bounds. As an empirical application, it gives new insights into the study of foreign occupation and insurgency in France.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples – the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Carbon credits from the reducing emissions from deforestation and degradation (REDD+) projects have been criticized for issuing junk carbon credits due to invalid ex-ante baselines. Recently, the concept of ex-post baseline has been discussed to overcome the criticism, while ex-ante baseline is still necessary for project financing and risk assessment. To address this issue, we propose a Bayesian state-space model that integrates ex-ante baseline projection and ex-post dynamic baseline updating in a unified manner. Our approach provides a tool for appropriate risk assessment and performance evaluation of REDD+ projects. We apply the proposed model to a REDD+ project in Brazil and show that it may have had a small, positive effect but has been overcredited. We also demonstrate that the 90% predictive interval of the ex-ante baseline includes the ex-post baseline, implying that our ex-ante estimation can work effectively.

The literature on populism is divided on whether economic factors are significant and robust causes of populism. To clarify this, we performed the first systematic review and meta-analysis of the evidence of a causal association between economic insecurity and populism. We combined database searches with searching the citations of eligible studies and recently published reviews. We identified and reviewed thirty-six studies and presented a concise narrative summary and numerical synthesis of the key findings. Although we found significant heterogeneity in several dimensions, all studies reported a significant causal association. A recurrent magnitude was that economic insecurity explained around one-third of recent surges in populism. We tested for publication bias by conducting a funnel-plot asymmetry test and a density discontinuity test of the distribution of t-statistics. We found significant evidence of publication bias; however, the causal association between economic insecurity and populism remains significant after controlling for it.

Some cultural evolutionary models predict that under stressful reductions of well-being, individuals will be more attracted and fastidiously adhere to traditional systems of norms that promote solidarity and cooperation. As religious systems can bolster human relationships with a variety of mechanisms, the material insecurity hypothesis of religion posits that individual religiosity will increase under conditions of material insecurity. The bulk of the literature up to this point has been correlational and cross-national. Here, across 14 field sites, we examine the causal role that educational attainment and food insecurity play in religiosity. We find that years of formal education and food insecurity do not consistently contribute to individual religiosity cross-culturally. We conclude with a discussion of some theoretical and methodological implications. As a general workflow for cross-cultural causal research in the quantitative social sciences, the present work is a modest but necessary first step in reliably estimating causation in the material insecurity hypothesis of religiosity.

Causal inference lies at the core of many scientific endeavours. Yet answering causal questions is challenging, especially when studying culture as a causal force. Against this backdrop, this paper reviews research designs and statistical tools that can be used – together with strong theory and knowledge about the context of study – to identify the causal impact of culture on outcomes of interest. We especially discuss how overlooked strategies in cultural evolutionary studies can allow one to approximate an ideal experiment wherein culture is randomly assigned to individuals or entire groups (instrumental variables, regression discontinuity design, and epidemiological approach). In doing so, we also review the potential outcome framework as a tool to engage in causal reasoning in the cultural evolutionary field.