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Activity Number: 77 - Causal Inference When Resources Are Limited
Type: Topic-Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #317575
Title: Nonparametric Identification and Estimation of Potential Outcomes Under Time-Varying Allocation of Binary Treatments
Author(s): Aaron Sarvet*
Companies: Harvard University
Keywords: potential outcomes; limited resources; policy evaluation; semiparametric estimation

The COVID-19 pandemic has created broad demand for scarce health care resources. Emerging scarcity requires new policies for triaging limited resources. In such settings, most sensible strategies may delay, rather than prevent, treatment reception for some patients. Additionally, when data arise from setting where treatment resources are newly limited, as in a crisis, standard assumptions on the independence of units are not satisfied. To make progress, we formulate a general potential-outcomes-based framework for evaluating the effects of strategies for allocating a fixed supply of limited resources in a time-varying setting. We provide non-parametric conditions that allow identification of counterfactual outcomes from observational data, and motivate semi-parametric estimators based on likelihood ratio weights. As an illustration, we consider estimation of survival under counterfactual rules for ventilator triage (including both initiation and termination) in an intensive care unit over the course a COVID-19 epidemic. We show that triage rules that optimize short-term survival may have sub-optimal survival by the end of a mass-casualty event in many settings.

Authors who are presenting talks have a * after their name.

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