# Uncovering Heterogeneity in Individual Treatment Responses via Causal Machine Learning

> **NIH NIH R35** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2024 · $417,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Clinical recommendations for the management of diseases are mostly based on the average treatment effects
observed in randomized controlled trials. However, the beneficial effects of most treatments vary across
individuals. Identifying the factors contributing to treatment response heterogeneity is crucial for improving
mechanistic understanding of treatment-disease interactions and optimizing patient outcomes. Recent
advances in high-throughput technologies in biology and the development of large-scale databases provide an
unprecedented opportunity for a more comprehensive understanding of mechanisms underlying inter-individual
variations in treatment responses. Several methods, including subgroup analyses and summary score-based
analyses, have been used to assess treatment response heterogeneity. To handle the high dimensionality of
covariates, machine learning methods have also been developed to assess treatment heterogeneity. However,
despite tremendous advancements in machine learning, two key limitations have hindered a large-scale
deployment of the current methods to discover markers underpinning treatment heterogeneity from big data.
First, the current approaches can fail to uncover strong but unexpected predictors of treatment response
heterogeneity. A key problem is that counterfactual treatment responses for an individual under two possible
strategies cannot be directly identified. To make progress, a common approach is to compare the average
observed treatment responses across subgroups of individuals, defined either based on one or multiple clinical
variables. Nonetheless, such approaches can fail to uncover true signatures for treatment heterogeneity.
Second, the current methods for predicting treatment heterogeneity often result in models with limited
generalizability. A key reason is that participants in the source population data (on which models are
developed) are not a random sample from the target population (on which models will be deployed). When the
source population data are not representative of the target population and treatment responses vary across
factors that influence participation, algorithms that can tailor the model for use in the new target population will
require cutting-edge tools in data science. To address these challenges, we propose novel causal machine
learning methods that will enable the identification of markers (and their complex relationships) for individual
treatment responses, with algorithms adaptable to a new target population. This project will combine
theoretical developments with large-scale simulation studies and empirical evaluations on treatment for
patients with stable coronary artery diseases. Successful completion of the proposed research will equip
investigators with powerful methods to unlock the full potential of big data, advance our understanding of
mechanisms for treatment response heterogeneity, and ultimately improve strategies for preventing and
managin...

## Key facts

- **NIH application ID:** 10940030
- **Project number:** 1R35GM154888-01
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Yu-Han Chiu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $417,000
- **Award type:** 1
- **Project period:** 2024-09-05 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10940030

## Citation

> US National Institutes of Health, RePORTER application 10940030, Uncovering Heterogeneity in Individual Treatment Responses via Causal Machine Learning (1R35GM154888-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10940030. Licensed CC0.

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