Conformal Inference of Counterfactuals and Individual Treatment Effects

• Activity Number: 457 - Conformal Prediction, Semiparametric Statistics, and Causal Inference
• Type: Invited
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Lihua Lei* and Emmanuel Candès
• Companies: Stanford University and Stanford University
• Keywords: conformal inference; counterfactual; individual treatment effect; uncertainty quantification; randomized experiment; observational study

Abstract

Evaluating treatment effect heterogeneity widely informs treatment decision making. In this work, we propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects under the potential outcome framework. For completely randomized or stratified randomized experiments with perfect compliance, the intervals have guaranteed average coverage in finite samples regardless of the unknown data generating mechanism. For randomized experiments with ignorable compliance and general observational studies obeying the strong ignorability assumption, the intervals satisfy a doubly robust property which states the following: the average coverage is approximately controlled if either the propensity score or the conditional quantiles of potential outcomes can be estimated accurately. Numerical studies on both synthetic and real datasets empirically demonstrate that existing methods suffer from a significant coverage deficit even in simple models. In contrast, our methods achieve the desired coverage with reasonably short intervals.