A simple nonparametric least-squares-based causal inference for heterogeneous treatment effects-Reference-Cited by-同舟云学术

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A simple nonparametric least-squares-based causal inference for heterogeneous treatment effects

Published:2024-07-15 Issue: Volume: Page:1-35
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Zhang Ying¹^ORCID,Xu Yuanfang²,Tong Lili¹,Bakoyannis Giorgos³,Huang Bin⁴

Affiliation:

1. Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA

2. Bristol Myers Squibb, Princeton, NY, USA

3. Department of Biostatistics and Health Data Science, Indiana University, Bloomington, IN, USA

4. Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA

Funder

National Institutes of Health

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2024.2367674

Reference52 articles.

1. Estimating Conditional Average Treatment Effects;Abrevaya J.;Journal of Business and Economic Statistics,2015

2. Recursive Partitioning for Heterogeneous Causal Effects;Athey S.;Proceedings of the National Academy of Sciences of the United States of America,2016

3. A Critical Appraisal of Propensity-score Matching in the Medical Literature Between 1996 and 2003;Austin P.;Statistics in Medicine,2008

4. Moving Towards Best Practice when Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies;Austin P.;Statistics in Medicine,2015

5. Instrumental Variable Methods for Causal Inference;Baiocchi M.;Statistics in Medicine,2014

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