Counterfactual risk assessments, evaluation, and fairness

By Amanda Coston, Alexandra Chouldechova, Edward H. Kennedy. 2019

135 citations recorded by the source corpus.

Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases, the purpose of the risk assessment tool is to inform actions, such as medical treatments or release conditions, often with the aim of reducing the likelihood of an adverse event such as hospital readmission or recidivism. Problematically, most tools are trained and evaluated on historical data in which the outcomes observed depend on the historical decision-making policy. These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform. Even when tools are constructed to predict risk under a specific decision, they are often improperly evaluated as predictors of the target outcome. Focusing on the evaluation task, in this paper we define counterfactual analogues of common predictive performance and algorithmic fairness metrics that we argue are better suited for the decision-making context. We introduce a new method for estimating the proposed metrics using doubly robust estimation. We provide theoretical results that show that only under strong conditions can fairness according to the standard metric and the counterfactual metric simultaneously hold. Consequently, fairness-promoting methods that target parity in a standard fairness metric may---and as we show empirically, do---induce greater imbalance in the counterfactual analogue. We provide empirical comparisons on both synthetic data and a real world child welfare dataset to demonstrate how the proposed method improves upon standard practice.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “More recent work has formalized this oversight using the language of counterfactuals and potential outcomes [20].”
Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, background): “However, evaluating a model on a population different than the one on which it will be used can be highly misleading, particularly with regards to predictive fairness measures (Kallus & Zhou, 2018; Coston et al., 2020).”
The Use and Misuse of Counterfactuals in Ethical Machine Learning (2021, background): “The use of counterfactuals has become increasingly popular in the machine learning community for many reasons such as making sense of algorithmic fairness or explainability in automated decision-making for consequential social contexts [4, 9, 14, 18, 29, 35, 42, 47, 51].”
Counterfactual Fairness Is Basically Demographic Parity (2022, background): “Other inﬂuential work takes a similar position (Gajane and Pechenizkiy 2017; Corbett-Davies and Goel 2018; Coston et al. 2020).”
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks (2021, background): “Coston et al. [61] develop counterfactual risk assessments to account for decisions.”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “More recent work has formalized this oversight using the language of counterfactuals and potential outcomes (Coston et al. 2020).”