Long Story Short: Omitted Variable Bias in Causal Machine Learning

Victor Chernozhukov develops sharp bounds on omitted-variable bias for a broad class of causal quantities. The framework covers averages of potential outcomes, average treatment effects, average derivatives, and policy effects generated by shifts in covariate distributions within general nonparametric causal models.

Using the Riesz–Fréchet representation of the target quantity, the analysis expresses the bias bound through the additional variation that unobserved variables introduce into the outcome and the relevant Riesz representer. Debiased machine learning then provides flexible statistical inference for the components of these bounds that can be learned from observed data. The approach connects sensitivity analysis for unmeasured confounding with modern causal estimation.