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

2026-09-15

AI Summary

研究在动态选择下处理时变协变量和潜在异质性对因果推断的影响，通过预处理结果历史识别相似个体，并提出基于核的双重稳健估计方法来解决动态平均处理效应的识别问题。

Abstract

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.

Problem

• dynamic treatment effects
• panel data
• time-varying covariates
• latent heterogeneity
• staggered adoption

Innovation

• dynamic treatment effects
• time-varying covariates
• latent heterogeneity
• doubly robust estimators
• double cross-fitting