Search

Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the effect modifier and other covariates, and including these interactions can lead to unstable estimates due to overfitting. In this paper, we explore the usefulness of machine learning algorithms for stabilizing these estimates and show how many off-the-shelf adaptive methods lead to two forms of bias: direct and indirect regularization bias. To overcome these issues, we use a post-double selection approach that utilizes several lasso estimators to select the interactions to include in the final model. We extend this approach to estimate uncertainty for both interaction and marginal effects. Simulation evidence shows that this approach has better performance than competing methods, even when the number of covariates is large. We show in two empirical examples that the choice of method leads to dramatically different conclusions about effect heterogeneity.

In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient structural breaks and perform inference on the number of such breaks in a given time series. I demonstrate the usefulness of the model with applications to campaign contributions in the 2012 U.S. Republican presidential primary and incidences of global terrorism from 1970 to 2015.

Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables researchers to rule out competing explanations—an important objective for political scientists. To estimate the controlled direct effect without bias, we describe an easy-to-implement estimation strategy from the biostatistics literature. We extend this approach by deriving a consistent variance estimator and demonstrating how to conduct a sensitivity analysis. Two examples—one on ethnic fractionalization’s effect on civil war and one on the impact of historical plough use on contemporary female political participation—illustrate the framework and methodology.

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some strong assumptions. In this paper, we use potential outcomes to define causal quantities of interest in these settings and clarify how standard models like the autoregressive distributed lag model can produce biased estimates of these quantities due to post-treatment conditioning. We then describe two estimation strategies that avoid these post-treatment biases—inverse probability weighting and structural nested mean models—and show via simulations that they can outperform standard approaches in small sample settings. We illustrate these methods in a study of how welfare spending affects terrorism.

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

In democratic countries, voting is one of the most important ways for citizens to influence policy and hold their representatives accountable. And yet, in the United States and many other countries, rates of voter turnout are alarmingly low. Every election cycle, mobilization efforts encourage citizens to vote and ensure that elections reflect the true will of the people. To establish the most effective way of encouraging voter turnout, this article seeks to differentiate between (1) the synergy hypothesis that multiple instances of voter contact increase the effectiveness of a single form of contact, and (2) the diminishing returns hypothesis that multiple instances of contact are less effective or even counterproductive. Remarkably, previous studies have been unable to compare these hypotheses because extant approaches to analyzing experiments with noncompliance cannot speak to questions of causal interaction. I resolve this impasse by extending the traditional instrumental variables framework to accommodate multiple treatment–instrument pairs, which allows for the estimation of conditional and interaction effects to adjudicate between synergy and diminishing returns. The analysis of two voter mobilization field experiments provides the first evidence of diminishing returns to follow-up contact and a cautionary tale about experimental design for these quantities. Supplementary materials for this article are available online.

Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and posttreatment bias. Thus, these kinds of “single-shot” causal inference methods are inappropriate for dynamic processes like campaigns. I resolve this dilemma by adapting methods from biostatistics, thereby presenting a holistic framework for dynamic causal inference. I then use this method to estimate the effectiveness of an inherently dynamic process: a candidate’s decision to “go negative.” Drawing on U.S. statewide elections (2000–2006), I find, in contrast to the previous literature and alternative methods, that negative advertising is an effective strategy for nonincumbents. I also describe a set of diagnostic tools and an approach to sensitivity analysis.