A common causal identification strategy in political science is selection on observables. This
strategy assumes one observes a set of covariates that is, after statistical adjustment …

Matching methods for causal inference selectively prune observations from the data in order
to reduce model dependence. They are successful when simultaneously maximizing …

The two-way linear fixed effects regression (2FE) has become a default method for
estimating causal effects from panel data. Many applied researchers use the 2FE estimator …

When estimating causal effects using observational data, it is desirable to replicate a
randomized experiment as closely as possible by obtaining treated and control groups with …

We study identification and estimation of causal effects in settings with panel data.
Traditionally, researchers follow model-based identification strategies relying on …

This paper reviews recent advances in the foundations of causal inference and introduces a
systematic methodology for defining, estimating, and testing causal claims in experimental …

Estimating the mechanisms that connect explanatory variables with the explained variable,
also known as “mediation analysis,” is central to a variety of social-science fields, especially …

Social scientists increasingly exploit natural experiments in their research. This article
surveys recent applications in political science, with the goal of illustrating the inferential …

Most work in causal inference considers deterministic interventions that set each unit's
treatment to some fixed value. However, under positivity violations these interventions can …

Political scientists have long been concerned about the validity of survey measurements.
Although many have studied classical measurement error in linear regression models where …