Title: Model-Robust Counterfactual Prediction Method,
Abstract: We develop a novel method for counterfactual analysis based on observational
data using prediction intervals for units under different exposures. Unlike
methods that target heterogeneous or conditional average treatment effects of
an exposure, the proposed approach aims to take into account the irreducible
dispersions of counterfactual outcomes so as to quantify the relative impact of
different exposures. The prediction intervals are constructed in a
distribution-free and model-robust manner based on the conformal prediction
approach. The computational obstacles to this approach are circumvented by
leveraging properties of a tuning-free method that learns sparse additive
predictor models for counterfactual outcomes. The method is illustrated using
both real and synthetic data.