research-article

Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes

Authors:

Xiao-Hua ZhouAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 2

Article No.: 19, Pages 1 - 15

https://doi.org/10.1145/2668135

Published: 03 December 2015 Publication History

Abstract

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this article, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.

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Cited By

Zhang YXuan YYadav ROmrani AFjeld M(2023)Playing with Data: An Augmented Reality Approach to Interact with Visualizations of Industrial Process TomographyHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_7(123-144)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42283-6_7
Woźniak MLewczuk AAdamkiewicz KJóziewicz JJaworski TRowińska Z(2020)ARchemistProceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia10.1145/3428690.3429168(58-63)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428690.3429168

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Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes
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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 2

Special Issue on Causal Discovery and Inference

January 2016

270 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2850424

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2015

Accepted: 01 September 2014

Revised: 01 June 2014

Received: 01 March 2014

Published in TIST Volume 7, Issue 2

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Cited By

Zhang YXuan YYadav ROmrani AFjeld M(2023)Playing with Data: An Augmented Reality Approach to Interact with Visualizations of Industrial Process TomographyHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_7(123-144)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42283-6_7
Woźniak MLewczuk AAdamkiewicz KJóziewicz JJaworski TRowińska Z(2020)ARchemistProceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia10.1145/3428690.3429168(58-63)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428690.3429168

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