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Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they often overlook the diversity of treatment effects across potential subgroups that have varying treatment effects and characteristics, treating the entire population as a homogeneous group. This limitation restricts the ability to precisely estimate treatment effects and provide targeted treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different responses and more precisely estimates treatment effects by considering subgroup-specific treatment effects in the estimation process. In addition, we introduce an expectation–maximization (EM)-based training process that iteratively optimizes estimation and subgrouping networks to improve both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets demonstrate the outstanding performance of SubgroupTE compared to the existing works for treatment effect estimation and subgrouping models. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing targeted treatment recommendations for patients with opioid use disorder (OUD) by incorporating subgroup identification with treatment effect estimation.
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Identifying and Estimating Causal Moderation for Treated and Targeted Subgroups
Extant literature on moderation effects narrowly focuses on the average moderated treatment effect across the entire sample (AMTE). Missing is the average moderated treatment effect on the treated (AMTT) and other targeted subgroups (AMTS). Much like the average treatment effect on the treated (ATT) for main effects, the AMTS changes the target of inferences from the entire sample to targeted subgroups. Relative to the AMTE, the AMTS is identified under weaker assumptions and often captures more policy-relevant effects. We present a theoretical framework that introduces the AMTS under the potential outcomes framework and delineates the assumptions for causal identification. We then propose a generalized propensity score method as a tool to estimate the AMTS using weights derived with Bayes Theorem. We illustrate the results and differences among the estimands using data from the Early Childhood Longitudinal Study. We conclude with suggestions for future research.
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- Award ID(s):
- 1913563
- PAR ID:
- 10391157
- Date Published:
- Journal Name:
- Multivariate Behavioral Research
- ISSN:
- 0027-3171
- Page Range / eLocation ID:
- 1 to 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/00273171.2022.2046997
