Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication—retrieving materials, reconstructing environments, executing code, and matching outputs to point estimates reported in regression tables. We define a universe of all empirical and quantitative papers from the three top political science journals (2010–2025) and measure stated data availability using automated extraction. For a stratified sample of 384 studies, we apply the workflow to conduct full-paper replication, totaling 3,382 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the full-paper reproducibility rate rises from 29.6% before DA-RT adoption to 79.8% after, and conditional on accessible replication packages, 94.4% of papers are fully reproducible (237/251). As a secondary application, we apply standardized IV diagnostics to 92 studies (215 specifications), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.

Theories of democratic diffusion predict that exposure to liberal democracies fosters support for political liberalism, while recent studies on Chinese overseas students suggest studying abroad generates backlash against liberal values. Using a unique four-year longitudinal dataset of nearly 1,000 Chinese college students in China and the United States, we examine how studying abroad shapes students’ ideology, attitudes, and identity. Our findings challenge both perspectives. Students who study in the United States are already more politically liberal, market-oriented, and cosmopolitan, and less nationalistic than their peers upon departure, underscoring the importance of selection. Ideological preferences remain remarkably stable throughout college, regardless of location. However, support for China’s political system and affective identification with Chinese national identity declines among students in both countries from 2022 to 2023, with a greater decline among students in the United States. These results show that selection, home-country shocks, and host-country context jointly shape overseas students’ political attitudes.

This Element offers a practical guide to estimating conditional marginal effects—how treatment effects vary with a moderating variable—using modern statistical methods. Commonly used approaches, such as linear interaction models, often suffer from unclarified estimands, limited overlap, and restrictive functional forms. This guide begins by clearly defining the estimand and presenting the main identification results. It then reviews and improves upon existing solutions, such as the semiparametric kernel estimator, and introduces robust estimation strategies, including augmented inverse propensity score weighting with Lasso selection (AIPW-Lasso) and double machine learning (DML) with modern algorithms. Each method is evaluated through simulations and empirical examples, with practical recommendations tailored to sample size and research context. All tools are implemented in the accompanying interflex package for R.

Two-way fixed effects (TWFE) models are widely used in political science to establish causality, but recent methodological discussions highlight their limitations under heterogeneous treatment effects (HTE) and violations of the parallel trends (PT) assumption. This growing literature has introduced numerous new estimators and procedures, causing confusion among researchers about the reliability of existing results and best practices. To address these concerns, we replicated and reanalyzed 49 studies from leading journals using TWFE models for observational panel data with binary treatments. Using six HTE-robust estimators, diagnostic tests, and sensitivity analyses, we find: (i) HTE-robust estimators yield qualitatively similar but highly variable results; (ii) while a few studies show clear signs of PT violations, many lack evidence to support this assumption; and (iii) many studies are underpowered when accounting for HTE and potential PT violations. We emphasize the importance of strong research designs and rigorous validation of key identifying assumptions. (Please see the Erratum, which addresses a typesetting error in the published article.)

In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form considerations, (ii) a focus on the importance of overlap in covariate distributions, (iii) the introduction of propensity score-based methods leading to doubly robust estimators, (iv) methods for estimating and exploiting treatment effect heterogeneity, and (v) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde data. We show that modern methods, when applied in contexts with sufficient covariate overlap, yield robust estimates for the adjusted differences between the treatment and control groups. However, this does not imply that these estimates are causally interpretable. To assess their credibility, validation exercises (such as placebo tests) are essential, whereas goodness-of-fit tests alone are inadequate. Our findings highlight the importance of closely examining the assignment process, carefully inspecting overlap, and conducting validation exercises when analyzing causal effects with nonexperimental data.

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships, but the identifying assumptions required by an IV design are demanding, and assessing their validity remains challenging. In this paper, we replicate 67 articles published in three top political science journals from 2010-2022 and identify several concerning patterns. First, researchers often overestimate the strength of their instruments due to non-i.i.d. error structures such as clustering. Second, the commonly used t-test for two-stage-least-squares (2SLS) estimates frequently underestimates uncertainties, resulting in uncontrolled Type-I errors in many studies. Third, in most replicated studies, 2SLS estimates are significantly larger than ordinary-least-squares estimates, with their ratio negatively correlated with instrument strength in studies with non-experimentally generated instruments, suggesting potential violations of unconfoundedness or the exclusion restriction. We provide a checklist and software to help researchers avoid these pitfalls and improve their practice.