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Political scientists often cite the importance of mechanism-specific causal knowledge, both for its intrinsic scientific value and as a necessity for informed policy. This article explains why two common inferential heuristics for mechanism-specific (i.e., indirect) effects can provide misleading answers, such as sign reversals and false null results, even when linear regressions provide unbiased estimates of constituent effects. Additionally, this article demonstrates that the inferential difficulties associated with indirect effects can be ameliorated with the use of stratification, interaction terms, and the restriction of inference to subpopulations (e.g., the indirect effect on the treated). However, indirect effects are inherently not identifiable-even when randomized experiments are possible. The methodological discussion is illustrated using a study on the indirect effect of Islamic religious tradition on democracy scores (due to the subordination of women). Adapted from the source document.

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.

AbstractWe develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Finally, we show that under one‐sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida's early in‐person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early in‐person voting had small positive effects on turnout in 2008. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.

In this article, we consider whether personal relationships can affect the way that judges decide cases. To do so, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more feminist fashion on gender issues than judges who have only sons. This result survives a number of robustness tests and appears to be driven primarily by Republican judges. More broadly, this result demonstrates that personal experiences influence how judges make decisions, and this is the first article to show that empathy may indeed be a component in how judges decide cases. Adapted from the source document.

Using the Rosenbaum (2002, 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p‐values. Second, additional information across matched sets enables the construction of qualitative confidence intervals on effect size. Third, qualitative information on unmeasured confounders within matched sets reduces the conservativeness of Rosenbaum‐style sensitivity analysis. This approach accommodates small to medium sample sizes in a nonparametric framework, and therefore it may be particularly useful for analyses of the effects of policies or institutions in a small number of units. We illustrate these methods by examining the effect of using plurality rules in transitional presidential elections on opposition harassment in 1990s sub‐Saharan Africa.

In many situations, data are available at some aggregate level, but one wishes to estimate the individual-level association between a response and an explanatory variable (or variables). Unfortunately, this endeavor is fraught with difficulties because of the ecological level of the data. The only reliable approach for overcoming the inherent identifiability problem associated with the analysis of ecological data is to supplement the ecological data with individual-level data. In this article, the authors illustrate the benefits of gathering individual-level data in the context of a Poisson modeling framework. Additionally, they derive optimal designs that allow the individual samples to be chosen so that information with respect to a particular model is maximized. The methods are illustrated using Robinson's classic data on illiteracy rates. The authors show that the optimal design, if used with an appropriate model, produces accurate inference with respect to estimation of relative risks, with ecological bias removed.