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

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