Ensemble Method for Estimating Individualized Treatment Effects
KDD Workshop on Causal Inference and Machine Learning in Practice, 2023
In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.
Recommended citation: Han, K. W., & Wu, H. (2022). Ensemble Method for Estimating Individualized Treatment Effects. arXiv preprint arXiv:2202.12445.