PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect

TL;DR

PairNet is a new training strategy for individual treatment effect estimation that leverages pairs of observed outcomes, leading to more accurate predictions and better generalization across various benchmarks.

Contribution

It introduces PairNet, a novel pair-based training method for ITE estimation that is theoretically consistent and empirically outperforms existing approaches.

Findings

PairNet achieves lower ITE error than baseline models.

It is model-agnostic and easy to implement.

Theoretical analysis confirms consistency and reduced generalization error.

Abstract

Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a covariate's outcome is observed only under one treatment, whereas we need to infer the difference in outcomes under two different treatments. Several existing approaches address this issue through training with inferred pseudo-outcomes, but their success relies on the quality of these pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy that minimizes losses over pairs of examples based on their factual observed outcomes. Theoretical analysis for binary treatments reveals that PairNet is a consistent estimator of ITE risk, and achieves smaller generalization error than baseline models. Empirical comparison with thirteen existing methods across eight benchmarks, covering both discrete and continuous treatments, shows that PairNet achieves significantly lower ITE error compared to the baselines. Also, it is model-agnostic and easy to implement.