On Causal Inference for the Survivor Function

TL;DR

This paper provides a comprehensive theoretical framework for causal inference of the survivor function, introducing new characterizations of coarsening at random and influence functions without continuity assumptions.

Contribution

It offers the first general characterization of coarsening at random and influence functions for the survivor function under sequential coarsening at random.

Findings

Established the first general characterization of coarsening at random.

Derived the nonparametric efficient influence function for the survivor function.

Proved the equivalence of influence functions with recent literature.

Abstract

In this expository paper, we consider the problem of causal inference and efficient estimation for the counterfactual survivor function. This problem has previously been considered in the literature in several papers, each relying on the imposition of conditions meant to identify the desired estimand from the observed data. These conditions, generally referred to as either implying or satisfying coarsening at random, are inconsistently imposed across this literature and, in all cases, fail to imply coarsening at random. We establish the first general characterization of coarsening at random, and also sequential coarsening at random, for this estimation problem. Other contributions include the first general characterization of the set of all influence functions for the counterfactual survival probability under sequential coarsening at random, and the corresponding nonparametric efficient influence function. These characterizations are general in that neither impose continuity assumptions on either the underlying failure or censoring time distributions. We further show how the latter compares to alternative forms recently derived in the literature, including establishing the pointwise equivalence of the influence functions for our nonparametric efficient estimator and that recently given in Westling et al (2024, Journal of the American Statistical Association).