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Activity Number: 195 - Topics in Personalized/Precision Medicine - II
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biopharmaceutical Section
Abstract #312537
Title: Matching-Based Classification Tree for Subgroup Effect Identification in Observational Data
Author(s): Bo Lu*
Companies: The Ohio State University
Keywords: causal inference; observational studies; subgroup effects; classification tree; matching
Abstract:

In observational studies, treatment is not randomly assigned to subjects. To estimate the causal effect, researchers need to account for the pretreatment covariate differences between treated and control groups. One popular adjustment strategy is through matching, which does not rely on parametric outcome modeling assumptions. When the treatment effects are heterogeneous among subpopulations, it is challenging to identify the subgroups (defined by covariates) with different effects. Ideally, the identification should be based on individual treatment effects, rather than the observed outcomes. We propose a matching-based classification tree strategy, which fits a tree model to the matched pair outcome differences. The simulation study demonstrates that our strategy outperforms other tree-based methods using observed outcomes in many scenarios.


Authors who are presenting talks have a * after their name.

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