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Published in: Lifetime Data Analysis 4/2023

09-05-2023

A nonparametric instrumental approach to confounding in competing risks models

Authors: Jad Beyhum, Jean-Pierre Florens, Ingrid Van Keilegom

Published in: Lifetime Data Analysis | Issue 4/2023

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Abstract

This paper discusses nonparametric identification and estimation of the causal effect of a treatment in the presence of confounding, competing risks and random right-censoring. Our identification strategy is based on an instrumental variable. We show that the competing risks model generates a nonparametric quantile instrumental regression problem. Quantile treatment effects on the subdistribution function can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which exact identification is possible and give partial identification results for other quantiles. We outline an estimation procedure and discuss its properties. The finite sample performance of the estimator is evaluated through simulations. We apply the proposed method to the Health Insurance Plan of Greater New York experiment.

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Appendix
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Metadata
Title
A nonparametric instrumental approach to confounding in competing risks models
Authors
Jad Beyhum
Jean-Pierre Florens
Ingrid Van Keilegom
Publication date
09-05-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 4/2023
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09599-3

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