# Efficient nonparametric estimation of heterogeneous treatment effects in causal inference

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $348,220

## Abstract

PROJECT SUMMARY
In nearly all studies of comparative effectiveness, the investigators seek to estimate how an in-
tervention changes outcomes on average. That is, are outcomes for treated subjects better on
average than for untreated subjects? While the average treatment effect (ATE) is a useful sum-
mary of the treatment effect, the treatment effect may vary from patient to patient. The ATE is
a low-dimensional summary of a treatment effect, since it summarizes the overall effect of the
treatment using a single quantitative measure and ignores possible effect heterogeneity. Many in-
vestigators seek to go beyond low-dimensional summaries by estimating heterogenous treatment
effects (HTEs). The most common approach to the estimation of HTEs relies on simple statistical
methods. Specifically, regression models are widely used but may be biased due to the linear
functional form especially where HTEs that are nonlinear or based on complex combinations of
patient subgroups. Currently, there is considerable interest in developing more flexible methods
for the estimation of the HTEs. In this project, we will use the the doubly robust machine learning
(DRML) framework to develop improved methods for a variety of HTEs. The DRML framework
is a combination of semiparametric theory, machine learning (ML) methods, and doubly robust
estimators. The key advantage of the DRML framework is that it allows one to reduce bias using
ML estimation methods, while retaining the efficiency of parametric models.

## Key facts

- **NIH application ID:** 10466912
- **Project number:** 5R01LM013361-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Luke Keele
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $348,220
- **Award type:** 5
- **Project period:** 2021-08-10 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10466912

## Citation

> US National Institutes of Health, RePORTER application 10466912, Efficient nonparametric estimation of heterogeneous treatment effects in causal inference (5R01LM013361-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10466912. Licensed CC0.

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