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

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