Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
This work addresses data efficiency in treatment effect estimation for fields like healthcare, where labeling costs are high, but it is incremental as it builds on existing active learning and counterfactual methods.
The paper tackles the problem of insufficient labeled data for treatment effect estimation by proposing an active learning method that maximizes factual and counterfactual coverage to reduce risk bounds, achieving superior performance on synthetic and semi-synthetic datasets compared to baselines.
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.