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Workshop: Algorithmic Fairness through the Lens of Causality and Privacy

A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making

Linying Zhang · Lauren Richter · Yixin Wang · Anna Ostropolets · Noemie Elhadad · David Blei · George Hripcsak


Fairness in clinical decision-making is a critical element of health equity, but assessing fairness of clinical decisions from observational data is challenging. Recently, many fairness notions have been proposed to quantify fairness in decision-making, among which causality-based fairness notions have gained increasing attention due to its potential in adjusting for confounding and reasoning about bias. However, causal fairness notions remain under-explored in the context of clinical decision-making with large-scale healthcare data. In this work, we propose a Bayesian causal inference approach for assessing a causal fairness notion called principal fairness in clinical settings. We demonstrate our approach using both simulated data and electronic health records (EHR) data.

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