Ensemble Method for Estimating Individualized Treatment Effects
- URL: http://arxiv.org/abs/2202.12445v2
- Date: Mon, 28 Feb 2022 01:51:17 GMT
- Title: Ensemble Method for Estimating Individualized Treatment Effects
- Authors: Kevin Wu Han and Han Wu
- Abstract summary: 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.
- Score: 15.775032675243995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In many medical and business applications, researchers are interested in
estimating individualized treatment effects using data from a randomized
experiment. For example in medical applications, doctors learn the treatment
effects from clinical trials and in technology companies, researchers learn
them from A/B testing experiments. Although dozens of machine learning models
have been proposed for this task, it is challenging to determine which model
will be best for the problem at hand because ground-truth treatment effects are
unobservable. 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.
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