Causal Inference under Dynamic Selection: Time-Varying Covariates and Latent Heterogeneity

I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent factors, and the key requirement is that these histories are sufficiently informative about those latent factors. I develop an identification strategy for the dynamic average treatment effect on the treated (ATT) and propose kernel-based doubly robust estimators for the dynamic ATT. I further combine double cross-fitting with undersmoothing and show that, under suitable regularity conditions, the proposed estimators are $\sqrt{n}$-consistent, asymptotically normal, and asymptotically unbiased. The simulation study demonstrates that the proposed method provides accurate inference across a wide range of data-generating processes. I illustrate the method with an application to the U.S. family planning program studied by Bailey (2012) and reestimate its effect on fertility rates.