GPMatch: A Bayesian causal inference approach using Gaussian process covariance function as a matching tool

A Gaussian process (GP) covariance function is proposed as a matching tool for causal inference within a full Bayesian framework under relatively weaker causal assumptions. We demonstrate that matching can be accomplished by utilizing GP prior covariance function to define matching distance. The mat...

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Bibliographic Details

Published in:	Frontiers in Applied Mathematics and Statistics
Main Authors:	Bin Huang, Chen Chen, Jinzhong Liu, Siva Sivaganisan
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2023-03-01
Subjects:	causal inference matching doubly robust (DR) estimator marginal structural model G-estimation real world data (RWD)
Online Access:	https://www.frontiersin.org/articles/10.3389/fams.2023.1122114/full