ORCID: https://orcid.org/0000-0001-7856-8729
(2022):
Estimating Conditional Average Treatment Effects with Missing Treatment Information.
AISTATS 2023, Valencia, 25.04.2023-27.04.2023.
arXiv.
Abstract
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where, thus, unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin.
Item Type: | Conference or Workshop Item (Paper) |
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Keywords: | Machine Learning (stat.ML); Machine Learning (cs.LG); FOS: Computer and information sciences; FOS: Computer and information sciences; Artificial Intelligence; AI, Künstliche Intelligenz; KI |
Faculties: | Munich School of Management > Institute of Artificial Intelligence (AI) in Management |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
Language: | English |
Item ID: | 94984 |
Date Deposited: | 09. Mar 2023, 09:09 |
Last Modified: | 09. Mar 2023, 09:09 |