Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach-Reference-Cited by-同舟云学术

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Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach

Published:2021-11-15 Issue: Volume: Page:1-14
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Kabata Daijiro¹²^ORCID,Shintani Mototsugu³^ORCID

Affiliation:

1. Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan

2. Department of Medical Statistics, Graduate School of Medicine, Osaka City University, Osaka, Japan

3. Faculty of Economics, The University of Tokyo, Tokyo, Japan

Funder

JSPS KAKENHI

Publisher

Informa UK Limited

Subject

Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2021.2001655

Reference28 articles.

1. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

2. Doubly Robust Estimation in Missing Data and Causal Inference Models

3. Do Instrumental Variables Belong in Propensity Scores?

4. Variable Selection for Propensity Score Models

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Response to correspondence concerning “Association between kidney function and intracerebral hematoma volume”;Journal of Clinical Neuroscience;2022-05

2. Association between kidney function and intracerebral hematoma volume;Journal of Clinical Neuroscience;2022-02

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