A Simple Measure of Robustness for External Validity under Covariate Shifts

Understand

This paper studies the robustness of estimated policy effects to changes in the distribution of covariates, a key determinant of the external validity of (quasi)-experimental results.

• I propose a novel robustness metric $\delta^*$ which measures the smallest covariate shift needed to invalidate an empirical claim about the policy effect (e.g., $ATE > 0$).
• I estimate $\delta^*$ via de-biased GMM, achieving a parametric rate of convergence while accommodating machine-learning estimators of treatment-effect heterogeneity (e.g., LASSO, random forests, neural networks).
• I develop benchmarking and calibration exercises to interpret the magnitude of $\delta^*$.