Zach Shahn - Structural Nested Mean Models Under Parallel Trends Assumptions (...solve all the problems in the DiD literature)
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October 02, 2026 2:00 pm - 3:00 pm ET
LGRT 1685
Title: Structural Nested Mean Models Under Parallel Trends Assumptions (...solve all the problems in the DiD literature)
Abstract: We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes—time-varying Difference in Differences (DiD; see Roth et al (2023) and Chaisemartin et al (2022) for reviews) and Structural Nested Mean Models (SNMMs; see Joffe and Vansteelandt (2014) for a review). In particular, we show that SNMMs, which were previously only known to be nonparametrically identified under a no unobserved confounding assumption, are also identified under a modified version of the parallel trends assumption typically used to justify time-varying DiD methods. Because SNMMs model a broader set of causal estimands, our results allow practitioners of existing time-varying DiD approaches to address additional types of substantive questions under similar assumptions. SNMMs enable estimation of time-varying effect heterogeneity, the lasting effects of a “blip” of treatment at a single time point, effects of sustained interventions (possibly on continuous or multi-dimensional treatments) when treatment repeatedly changes value in the data, controlled direct effects, effects of dynamic treatment strategies that depend on covariate history, and more. Our results also allow analysts who apply SNMMs under the no unobserved confounding assumption to estimate some of the same causal effects under alternative identifying conditions and thus potentially to triangulate evidence. We provide a method for sensitivity analysis to violations of our parallel trends assumption and placebo effect diagnostics. We further explain how to estimate optimal treatment regimes via optimal regime SNMMs under parallel trends assumptions plus an assumption that there is no effect modification by unobserved confounders. Finally, we illustrate our methods with real data applications estimating effects of Medicaid expansion on uninsurance rates, effects of floods on flood insurance take-up, and effects of sustained changes in temperature on crop yields.
Short Bio: Zach Shahn is an Assistant Professor in the Department of Epidemiology and Biostatistics at CUNY Graduate School of Public Health and Health Policy. His research interests are in development and thoughtful application of causal inference methodology.