Sensitivity analysis under the $f$-sensitivity models: a distributional robustness perspective

TL;DR

This paper develops a new $f$-sensitivity model for causal inference that captures both the magnitude and likelihood of unmeasured confounding, providing a distributionally robust framework for sensitivity analysis with proven statistical properties.

Contribution

It introduces the $f$-sensitivity model, linking bounds on counterfactuals to distributionally robust optimization, and proposes novel estimators with theoretical guarantees.

Findings

The proposed estimators converge to valid bounds under consistent nuisance estimation.

The $f$-sensitivity model captures both magnitude and probability of unmeasured confounding.

Numerical experiments demonstrate the effectiveness of the method.

Abstract

This paper introduces the $f$-sensitivity model, a new sensitivity model that characterizes the violation of unconfoundedness in causal inference. It assumes the selection bias due to unmeasured confounding is bounded "on average"; compared with the widely used point-wise sensitivity models in the literature, it is able to capture the strength of unmeasured confounding by not only its magnitude but also the chance of encountering such a magnitude. We propose a framework for sensitivity analysis under our new model based on a distributional robustness perspective. We first show that the bounds on counterfactual means under the f-sensitivity model are optimal solutions to a new class of distributionally robust optimization (DRO) programs, whose dual forms are essentially risk minimization problems. We then construct point estimators for these bounds by applying a novel debiasing technique to the output of the corresponding empirical risk minimization (ERM) problems. Our estimators are shown to converge to valid bounds on counterfactual means if any nuisance component can be estimated consistently, and to the exact bounds when the ERM step is additionally consistent. We further establish asymptotic normality and Wald-type inference for these estimators under slower-than-root-n convergence rates of the estimated nuisance components. Finally, the performance of our method is demonstrated with numerical experiments.