Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
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Abstract (arXiv)
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 -- textitfactual and textitcounterfactual 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 textitFactual and textitCounterfactual 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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 58 references that are in the map. First posted on arXiv 8 May 2025.
Builds on
30 of 58 references.
References · 30
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18 more
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.