As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…

Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do…

Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units…

Frequentists' inference often delivers point estimators associated with confidence intervals or sets for parameters of interest. Constructing the confidence intervals or sets requires understanding the sampling distributions of the point estimators, which, in many but not all cases, are related to asymptotic Normal distributions ensured by central…

In this article, we review popular parametric models for analyzing panel data and introduce the latest advances in matching methods for panel data analysis. To the extent that the parametric models and the matching methods offer distinct advantages for drawing causal inference, we suggest using both to cross-validate the evidence. We demonstrate…

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Regression artifacts are a source of mistaken causal inference in inferences based on time-series data and from longitudinal studies. These artifacts are illustrated, and it is noted that their magnitude is computable (and distinguishable from genuine effects) if the autocorrelation patterns for various lags is known. (SLD)

Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate…

In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the…