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Inference for Group Interaction Experiments
Working paper with Jiawei Fu and Cyrus Samii Download the manuscript here Why this paper matters Group interaction experiments—such as deliberation studies, classroom experiments, group therapy sessions, and lab coordination games—are widely used across political science, economics, psychology, and public health. Yet researchers often analyze these designs using off-the-shelf methods (individual-level regressions, cluster-robust standard errors, or simple difference-in-means)

Ye Wang
Jul 22 min read


Why Women and Men Vote Differently: The Gender Gap on the Sociocultural Cleavage
Working paper with Jonne Kamphorst, Liesbet Hooghe, and Gary Marks Download the manuscript here This paper investigates how and why the contemporary gender gap in voting emerges—specifically, why women are more likely to support GAL (Green–Alternative–Liberal) parties and less likely to support TAN (Traditional–Authoritarian–Nationalist) parties than men. Grounded in social role theory, we argue that the gender gap is not solely rooted in early-life traits or stable psycholog

Ye Wang
Jul 22 min read


Causal Inference in Longitudinal Data under Unknown Interference
Working paper, with Michael Jetsupphasuk; an earlier version of the paper is entitled "Causal Inference under Temporal and Spatial Interference." Download the manuscript here Why this paper matters Longitudinal causal inference in biostatistics, epidemiology, and social sciences often relies on marginal structural models (MSMs), which summarize the effects of time-varying treatments and can be consistently estimated using inverse probability weighting (IPW) under sequential

Ye Wang
Dec 2, 20252 min read


The Impact of US–China Tensions on US Science: Evidence from the NIH Investigations
PNAS 2023, with Ruixue Jia , Margaret Roberts , and Eddie Yang Download the manuscript here This paper documents how U.S.–China political tensions have reshaped scientific production in the United States. We study NIH-initiated investigations that began in 2018 and targeted undisclosed foreign funding, with most cases involving China. Using large-scale publication data from 2010–2021, we compare U.S. life scientists who had collaborated with researchers in China to similar s

Ye Wang
Dec 2, 20251 min read


Design-Based Inference for Spatial Experiments with Interference
Annals of Applied Statistics, 2025, with Cyrus Samii , Haoge Chang, and P Aronow . Why this paper matters Many policy experiments and field interventions, such as forest conservation programs, public health campaigns, and policing deployments, take place in geographic space. In these settings, treatment at one location can affect outcomes nearby, creating spatial interference that violates the stable unit treatment value assumption (SUTVA). Existing solutions to this proble

Ye Wang
Dec 1, 20252 min read


Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
Political Analysis, 2025, with Cyrus Samii and Junlong Aaron Zhou . Why this problem matters Many experiments and quasi-experiments suffer from the problem of endogenously missing outcomes : researchers only observe results for people who complete a key task, such as a survey, conversation, or interview, while dropout or nonresponse itself can be affected by the treatment. This creates what Slough (2023) calls the “phantom counterfactual” issue, where some units have undef

Ye Wang
Jul 10, 20212 min read


Electoral Impacts of A Failed Uprising: Evidence from Hong Kong’s Umbrella Movement
Electoral Studies, 2021 , with Stan Wong Download the manuscript here This paper examines how a major anti-regime protest—the 2014 Umbrella Movement in Hong Kong—shaped electoral outcomes in the city. Using fine-grained constituency-level election data, we show that citizens living closer to the protest sites were more likely to shift away from the pro-democracy opposition in the subsequent legislative election. A one–standard deviation decrease in distance to the protest sit

Ye Wang
Jan 16, 20202 min read


A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data
American Journal of Political Science, 2024, with Licheng Liu and Yiqing Xu Download the manuscript here Why this paper matters This paper provides a practical framework for causal inference with time-series cross-sectional (TSCS) data that addresses well-known problems with the standard two-way fixed effects (TWFE) model. TWFE is popular because it controls for unit and time fixed effects, but it relies on assumptions such as strict exogeneity, constant treatment effects, a

Ye Wang
Jan 15, 20201 min read


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