Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates

TL;DR

This paper introduces a conformal inference method for individual treatment effects that uses conditional density estimates to produce valid and narrower prediction intervals, improving precision over existing approaches.

Contribution

It proposes a novel conformal inference approach leveraging conditional density estimates for more accurate and less conservative ITE prediction intervals.

Findings

Prediction intervals are valid and narrower than existing methods.

Experimental results confirm the effectiveness of the proposed approach.

The method efficiently estimates conditional densities using a two-stage conformal framework.

Abstract

In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and public policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal Quantile Regression (CQR) and related techniques, often yield overly conservative prediction intervals. In this work, we introduce a conformal inference approach to ITE using the conditional density of the outcome given the covariates. We leverage the reference distribution technique to efficiently estimate the conditional densities as the score functions under a two-stage conformal ITE framework. We show that our prediction intervals are not only marginally valid but are narrower than existing methods. Experimental results further validate the usefulness of our method.