Neyman-Orthogonal Changes-in-Changes Estimators for Causal Inference in Panel Data

Music City Center 

We build on the influential changes‐in‐changes (CiC) framework of Athey and Imbens (2006) to estimate the average treatment effect on the treated (ATT) in panel‐data settings with unmeasured confounding. CiC has been a powerful alternative to difference‐in‐differences (DiD), as it relaxes the parallel‐trends requirement. At the same time, most existing implementations of CiC assume (i) a scalar unobserved confounder and (ii) a monotonic relation between that confounder and the outcome. To broaden the applicability of CiC, we make two key contributions. First, we show that the ATT remains nonparametrically identified under a set of novel conditions that allow for multivariate or non‐monotonic unmeasured confounders. Second, we propose a semiparametric estimator that is Neyman orthogonal with respect to infinite‐dimensional nuisance functions. This estimator can be applied with continuous measured covariates and modern machine‐learning tools while preserving valid inference. We illustrate our approach by studying how mass shootings influence voter behavior in U.S. presidential elections, a setting where voter sentiment is complex and only partly observed.

Difference-in-Differences

Unmeasured Confounding

Semiparametric Theory

Policy Evaluation 

Section on Statistics in Epidemiology