# Model-Robust Counterfactual Prediction Method

## TL;DR

This paper introduces a robust, distribution-free method for counterfactual prediction that accounts for outcome variability and leverages conformal prediction and sparse additive models, improving impact quantification.

## Contribution

The paper presents a novel, model-robust counterfactual prediction approach using conformal prediction and sparse additive models, addressing computational challenges and outcome dispersion.

## Key findings

- Effective in real and synthetic data scenarios
- Quantifies impact of exposures considering outcome variability
- Distribution-free and model-robust prediction intervals

## 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.