Partial causal identification for right censored data with noncompliance

Yang Bai and Yifan Cui

Journal of Nonparametric Statistics, 2025, vol. 37, issue 4, 1182-1204

Abstract: Medical researchers are typically interested in determining the effect of an intervention on the survival time that can be right censored, and the censoring time is usually treated as independent of the survival time. However, the independent censoring assumption is untestable and might bias the inference if taken on faith. Furthermore, in observational studies or randomised experiments with noncompliance when unconfoundedness does not necessarily hold, causal effects might only be partially identified, e.g. using instrumental variables. In this paper, we develop instrumental partial identification approaches for causal effect estimation for right censored observational data, where we aim to estimate the effect of an intervention on survival probabilities under an arbitrary dependence structure between survival and censoring time. We illustrate the proposed interval estimators in a variety of settings. We also demonstrate our method by a data application to evaluate the effect of having more than one living child on elderly parental survival probabilities.

Date: 2025
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DOI: 10.1080/10485252.2025.2452225

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