A Bayesian nonparametric approach to causal inference on quantiles

Biometrics
Dandan XuAlmut G Winterstein

Abstract

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records.

References

Oct 4, 2007·Statistical Applications in Genetics and Molecular Biology·Mark J van der LaanAlan E Hubbard
Jun 3, 2008·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Anastasios A Tsiatis, Marie Davidian
Sep 19, 2008·Biometrika·David B Dunson, Ju-Hyun Park
Sep 28, 2010·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Elizabeth A Stuart
Dec 14, 2011·Biometrics·Zhiwei ZhangJun Zhang
Feb 6, 2013·Biometrics·Corwin M ZiglerFrancesca Dominici
Dec 1, 2009·Journal of the American Statistical Association·Yeonseung Chung, David B Dunson
Apr 4, 2014·Journal of the American Statistical Association·Corwin Matthew Zigler, Francesca Dominici
Mar 17, 2016·Biostatistics·Dandan XuAlmut G Winterstein
Sep 12, 2017·Journal of the American Statistical Association·Kristin A LinnLeonard A Stefanski

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Citations

Aug 29, 2020·Statistics in Medicine·Yuying XieYeying Zhu
Mar 13, 2019·Statistics in Medicine·Estelina S M CapistranoAlexandra M Schmidt
Jan 1, 2019·Journal of the American Statistical Association·Joseph Antonelli, Michael J Daniels
Aug 28, 2021·Statistical Methods in Medical Research·Sarah B PeskoeSebastien Haneuse

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