Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective

TL;DR

This paper introduces a novel active learning framework for treatment effect estimation that minimizes labeling costs by optimizing factual and counterfactual coverage, supported by theoretical analysis and empirical validation.

Contribution

It formalizes treatment effect estimation as an active learning problem using coverage radii, and proposes FCCM, a new algorithm for effective data acquisition under budget constraints.

Findings

FCCM outperforms baselines on synthetic datasets

Theoretical bounds guide effective data selection

Proposed methods reduce labeling costs significantly

Abstract

Although numerous complex algorithms for treatment effect estimation have been developed in recent years, their effectiveness remains limited when handling insufficiently labeled training sets due to the high cost of labeling the effect after treatment, e.g., expensive tumor imaging or biopsy procedures needed to evaluate treatment effects. Therefore, it becomes essential to actively incorporate more high-quality labeled data, all while adhering to a constrained labeling budget. To enable data-efficient treatment effect estimation, we formalize the problem through rigorous theoretical analysis within the active learning context, where the derived key measures -- \textit{factual} and \textit{counterfactual covering radius} determine the risk upper bound. To reduce the bound, we propose a greedy radius reduction algorithm, which excels under an idealized, balanced data distribution. To generalize to more realistic data distributions, we further propose FCCM, which transforms the optimization objective into the \textit{Factual} and \textit{Counterfactual Coverage Maximization} to ensure effective radius reduction during data acquisition. Furthermore, benchmarking FCCM against other baselines demonstrates its superiority across both fully synthetic and semi-synthetic datasets.