A nonparametric instrumental approach to confounding in competing risks models

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 nonpar...

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Bibliographic Details
Published in:Lifetime data analysis 2023-10, Vol.29 (4), p.709-734
Main Authors: Beyhum, Jad, Florens, Jean-Pierre, Van Keilegom, Ingrid
Format: Article
Language:English
Subjects:
Breast cancer
Economics
Experiments
Finance
Health insurance
Health Sciences
Identification
Insurance
Management
Mathematics and Statistics
Medicine
Nonparametric statistics
Operations Research/Decision Theory
Quality Control
Quantiles
Reliability
Safety and Risk
Simulation
Statistics
Statistics for Business
Statistics for Life Sciences
Womens health
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Summary: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.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-023-09599-3