We introduce hierarchically regularized entropy balancing as an extension to entropy balancing, a reweighting method that adjusts weights for control group units to achieve covariate balance in observational studies with binary treatments. Our proposed extension expands the feature space by including higher-order terms (such as squared and cubic terms and interactions) of covariates and then achieves approximate balance on the expanded features using ridge penalties with a hierarchical structure. Compared with entropy balancing, this extension relaxes model dependency and improves the robustness of causal estimates while avoiding optimization failure or highly concentrated weights. It prevents specification searches by minimizing user discretion in selecting features to balance on and is also computationally more efficient than kernel balancing, a kernel-based covariate balancing method. We demonstrate its performance through simulations and an empirical example. We develop an open-source R package, hbal, to facilitate implementation.

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes like educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects from randomized controlled trials (RCTs). Both average treatment effects from RCTs and within-family genetic effects are shallow causes: they operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.

While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation accuracy, and (3) allowing for a more flexible parallel trends assumption. We then propose a new estimator, double DID, which combines all the benefits through the generalized method of moments and contains the two-way fixed effects regression as a special case. We show that the double DID requires a weaker assumption about outcome trends and is more efficient than existing DID estimators. We also generalize the double DID to the staggered adoption design where different units can receive the treatment in different time periods. We illustrate the proposed method with two empirical applications, covering both the basic DID and staggered adoption designs. We offer an open-source R package that implements the proposed methodologies.

A core part of political research is to identify how political preferences are shaped. The nature of these questions is such that robust causal identification is often difficult to achieve, and we are not seldom stuck with observational methods that we know have limited causal validity. The purpose of this paper is to measure the magnitude of bias stemming from both measurable and unmeasurable confounders across three broad domains of individual determinants of political preferences: socio-economic factors, moral values, and psychological constructs. We leverage a unique combination of rich Swedish registry data for a large sample of identical twins, with a comprehensive battery of 34 political preference measures, and build a meta-analytical model comparing our most conservative observational (naive) estimates with discordant twin estimates. This allows us to infer the amount of bias from unobserved genetic and shared environmental factors that remains in the naive models for our predictors, while avoiding precision issues common in family-based designs. The results are sobering: in most cases, substantial bias remains in naive models. A rough heuristic is that about half of the effect size even in conservative observational estimates is composed of confounding.

We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.

In light of important political events that go beyond the nation state (e.g., migration, climate change, and the coronavirus pandemic), domestic politicians are increasingly pressured to scrutinize and speak out on European policy-making. This creates a potential trade-off between allocating effort to domestic and supranational affairs, respectively. We examine how citizens perceive legislator involvement in European Union (EU) politics with a pre-registered conjoint experiment in Germany. Our results show that Members of Parliament (MPs) are not disadvantaged when allocating effort to European affairs as compared to local and national affairs. In addition, voters tend to prefer MPs who engage in EU policy reform over those who do not. As demand for legislator involvement in European politics is on the rise, we provide empirical evidence that MPs can fulfill this demand without being disadvantaged by the electorate.

In a recent article, I argued that the Bayesian process tracing literature exhibits a persistent disconnect between principle and practice. In their response, Bennett, Fairfield, and Charman raise important points and interesting questions about the method and its merits. This letter breaks from the ongoing point-by-point format of the debate by asking one question: In the most straightforward case, does the literature equip a reasonable scholar with the tools to conduct a rigorous analysis? I answer this question by walking through a qualitative Bayesian analysis of the simplest example: analyzing evidence of a murder. Along the way, I catalogue every question, complication, and pitfall I run into. Notwithstanding some important clarifications, I demonstrate that aspiring practitioners are still facing a method without guidelines or guardrails.

This paper proposes a Bayesian alternative to the synthetic control method for comparative case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive approach to Rubin’s causal model, which allows researchers to make inferences about both individual and average treatment effects on treated observations based on the empirical posterior distributions of their counterfactuals. The prediction model we develop is a dynamic multilevel model with a latent factor term to correct biases induced by unit-specific time trends. It also considers heterogeneous and dynamic relationships between covariates and the outcome, thus improving precision of the causal estimates. To reduce model dependency, we adopt a Bayesian shrinkage method for model searching and factor selection. Monte Carlo exercises demonstrate that our method produces more precise causal estimates than existing approaches and achieves correct frequentist coverage rates even when sample sizes are small and rich heterogeneities are present in data. We illustrate the method with two empirical examples from political economy.

Regression discontinuity (RD) designs have become increasingly popular in political science, due to their ability to showcase causal effects under weak assumptions. This paper provides an intuition-based guide for the use of the RD in applied research. After an intuitive explanation of how the method works, we provide a checklist that can help researchers understand the main robustness checks they should run, and a quick introduction to software implementing the design. We also provide a list of classic designs and examples of their application in political science. We hope this article can constitute a stepping stone from which researchers interested in RD can jump to more advanced literature; and which makes researchers not interested in implementing RDs better consumers of research employing this design.