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Activity Number: 284 - Learning Individualized/Sub-Group Treatment Rules in Complex Settings
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Health Policy Statistics Section
Abstract #317563
Title: Automatic Regression Discontinuity Discovery Using Tree-Based Partitioning
Author(s): Tony Liu* and Rahul Ladhania and Lyle Ungar and Konrad Kording
Companies: University of Pennsylvania and University of Michigan and University of Pennsylvania and University of Pennsylvania
Keywords: regression discontinuity design; tree-based partitioning; claims data; treatment assignment

The regression discontinuity design (RDD) is a popular technique for estimating causal effects from observational data by exploiting changes in treatment assignment according to hard thresholds of a continuous variable. However, identifying candidate RDDs is a largely manual process requiring domain knowledge, so it would be useful to have data-driven methods for finding interpretable RDDs. Moreover, disparities could exist in how different sub-groups are assigned treatments – for instance, pain thresholds for treatment referral could vary, implicitly and often unfairly, by gender or ethnicity. The authors propose a method for an automated multi-dimensional RDD discovery through a tree-based partitioning approach, and validate its effectiveness through simulated and real claims data.

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

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