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Journal of Causal Inference

Ed. by Imai, Kosuke / Pearl, Judea / Petersen, Maya Liv / Sekhon, Jasjeet / van der Laan, Mark J.

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2193-3685
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Bayesian Inference of Causal Effects for an Ordinal Outcome in Randomized Trials

Yasutaka Chiba
Published Online: 2018-05-23 | DOI: https://doi.org/10.1515/jci-2017-0019

Abstract

In randomized trials in which two treatment arms are compared with a binary outcome, the causal effect can be identified by assuming that the two treatment arms are exchangeable. In trials with an ordinal outcome, which is categorized as more than two, the causal effect can be identified by assuming that the potential outcomes are independent and that the two treatment arms are exchangeable. In this article, we propose a Bayesian approach to causal inference that does not rely on these two assumptions. To achieve this purpose, we use a randomization-based approach and response type. Then, the likelihood function is derived by physical randomization in which subjects who belong to a response type are randomly assigned to the treatment or control, with no modeling assumption on the outcome. Our approach can derive not only the posterior distribution of the causal effect but also that of the number of subjects in each response type. The proposed approach is illustrated with two examples from randomized clinical trials.

Keywords: Contingency Table; Potential Outcome; Response Type

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About the article

Received: 2017-08-04

Revised: 2018-04-25

Accepted: 2018-05-03

Published Online: 2018-05-23


Funding Source: Japan Society for the Promotion of Science

Award identifier / Grant number: 15K00057

This work was supported partially by Grant-in-Aid for Scientific Research (No. 15K00057) from Japan Society for the Promotion of Science.


Citation Information: Journal of Causal Inference, 20170019, ISSN (Online) 2193-3685, DOI: https://doi.org/10.1515/jci-2017-0019.

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