An instrumental variable approach under dependent censoring

Gilles Crommen (), Jad Beyhum () and Ingrid Van Keilegom ()
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Gilles Crommen: KU Leuven
Jad Beyhum: KU Leuven
Ingrid Van Keilegom: KU Leuven

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 2, No 5, 473-495

Abstract: Abstract This paper considers the problem of inferring the causal effect of a variable Z on a dependently censored survival time T. We allow for unobserved confounding variables, such that the error term of the regression model for T is dependent on the confounded variable Z. Moreover, T is subject to dependent censoring. This means that T is right censored by a censoring time C, which is dependent on T (even after conditioning out the effects of the measured covariates). A control function approach, relying on an instrumental variable, is leveraged to tackle the confounding issue. Further, it is assumed that T and C follow a joint regression model with bivariate Gaussian error terms and an unspecified covariance matrix, such that the dependent censoring can be handled in a flexible manner. Conditions under which the model is identifiable are given, a two-step estimation procedure is proposed, and it is shown that the resulting estimator is consistent and asymptotically normal. Simulations are used to confirm the validity and finite-sample performance of the estimation procedure. Finally, the proposed method is used to estimate the causal effect of job training programs on unemployment duration.

Keywords: Dependent censoring; Causal inference; Instrumental variable; Control function; Survival analysis; 62N02; 62F12; 62D20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-023-00903-9

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