# Counterfactual Reasoning for Fair Clinical Risk Prediction

arXiv: https://arxiv.org/abs/1907.06260 · HTML: https://mlsanity.com/paper/1907.06260/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2019.

## Built on by (most cited first, 28 of 28)

- [Fast Model Debias with Machine Unlearning](https://mlsanity.com/paper/2310.12560/) (2023)
- [A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models](https://mlsanity.com/paper/2102.06648/) (2021)
- [Fairness without the sensitive attribute via Causal Variational Autoencoder](https://mlsanity.com/paper/2109.04999/) (2021)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Counterfactual Fairness Is Basically Demographic Parity](https://mlsanity.com/paper/2208.03843/) (2022)
- [Counterfactually Fair Representation](https://mlsanity.com/paper/2311.05420/) (2023)
- [Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments](https://mlsanity.com/paper/2309.13005/) (2023)
- [Mitigating Health Disparities in EHR via Deconfounder](https://mlsanity.com/paper/2210.15901/) (2022)
- [LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching](https://mlsanity.com/paper/2510.14623/) (2025)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise](https://mlsanity.com/paper/2307.04868/) (2023)
- [Causal Fair Machine Learning via Rank-Preserving Interventional Distributions](https://mlsanity.com/paper/2307.12797/) (2023)
- [Consistent End-to-End Estimation for Counterfactual Fairness](https://mlsanity.com/paper/2310.17687/) (2023)
- [Addressing Both Statistical and Causal Gender Fairness in NLP Models](https://mlsanity.com/paper/2404.00463/) (2024)
- [Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness](https://mlsanity.com/paper/2106.08161/) (2021)
- [The Fairness Field Guide: Perspectives from Social and Formal Sciences](https://mlsanity.com/paper/2201.05216/) (2022)
- [Causal Fairness Assessment of Treatment Allocation with Electronic Health Records](https://mlsanity.com/paper/2211.11183/) (2022)
- [Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets](https://mlsanity.com/paper/2504.11504/) (2025)
- [Hierarchical Variational Autoencoder for Visual Counterfactuals](https://mlsanity.com/paper/2102.00854/) (2021)
- [A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder](https://mlsanity.com/paper/2308.11819/) (2023)
- [Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2503.04946/) (2025)
- [Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference](https://mlsanity.com/paper/2602.12542/) (2026)
- [Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study](https://mlsanity.com/paper/2607.06603/) (2026)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)
- [Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing](https://mlsanity.com/paper/2408.02558/) (2024)
- [Anterior's Approach to Fairness Evaluation of Automated Prior Authorization System](https://mlsanity.com/paper/2603.14631/) (2026)
- [Integrating Causal Machine Learning into Clinical Decision Support Systems: Insights from Literature and Practice](https://mlsanity.com/paper/2603.24448/) (2026)
- [Proxy reliance in large language model decisions is uncalibrated to predictive evidence](https://mlsanity.com/paper/2608.22887/) (2026)

## Abstract (arXiv)

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, and a counterfactual patient resulting from changing a sensitive attribute, if the factual and counterfactual outcomes do not differ. We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data. As the fairness criteria is ill-defined without knowledge of the data generating process, we use a variational autoencoder to perform counterfactual inference in the context of an assumed causal graph. While our technique provides a means to trade off maintenance of fairness with reduction in predictive performance in the context of a learned generative model, further work is needed to assess the generality of this approach.