Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be

Our empirical findings support recommendations to build ensemble learners for doubly robust estimators using a diverse array of flexible machine learning algorithms.

Authors

Lisa M. Bodnar (ORCID: https://orcid.org/0000-0001-9427-5467)
Ashley Isaac Naimi (ORCID: https://orcid.org/0000-0002-1510-8175)
Zhaohua Zeng

Institutions

Emory University (US)
University of Pittsburgh (US)

Publication Details

Journal: Epidemiology
Published: 2025-08-15
DOI: https://doi.org/10.1097/ede.0000000000001906
Citations: 1
Primary Topic: Advanced Causal Inference Techniques
Type: article
Field-Weighted Citation Impact: 2.63

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article

Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be

Lisa M. Bodnar, Ashley Isaac Naimi, Zhaohua Zeng

Aug 15, 2025

1 citations

Epidemiology

Advanced Causal Inference Techniques

2.63

article

Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be

Lisa M. Bodnar, Ashley Isaac Naimi, Zhaohua Zeng

August 15, 2025article en

1 citations

Abstract

Our empirical findings support recommendations to build ensemble learners for doubly robust estimators using a diverse array of flexible machine learning algorithms.

EpidemiologyVol. 36(6)

Emory University (US), University of Pittsburgh (US)

Good health and well-being

Openalex Percentile: Top 9%

Advanced Causal Inference Techniques

2.63

DOI Source OpenAlex

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Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be — Lisa M. Bodnar, Ashley Isaac Naimi, et al. · Epidemiology (2025) | TGRS Research Map | TGRS