# Equal Opportunity and Affirmative Action via Counterfactual Predictions

arXiv: https://arxiv.org/abs/1905.10870 · HTML: https://mlsanity.com/paper/1905.10870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 11 of 11)

- [Causal Conceptions of Fairness and their Consequences](https://mlsanity.com/paper/2207.05302/) (2022)
- [Auditing the Use of Language Models to Guide Hiring Decisions](https://mlsanity.com/paper/2404.03086/) (2024)
- [Breaking Feedback Loops in Recommender Systems with Causal Inference](https://mlsanity.com/paper/2207.01616/) (2022)
- [FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes](https://mlsanity.com/paper/2109.00173/) (2021)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [On Learning and Testing of Counterfactual Fairness through Data Preprocessing](https://mlsanity.com/paper/2202.12440/) (2022)
- [Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations](https://mlsanity.com/paper/2310.01684/) (2023)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [Counterfactual Fairness through Transforming Data Orthogonal to Bias](https://mlsanity.com/paper/2403.17852/) (2024)
- [New User Event Prediction Through the Lens of Causal Inference](https://mlsanity.com/paper/2407.05625/) (2024)
- [Mitigating Label Bias with Interpretable Rubric Embeddings](https://mlsanity.com/paper/2605.21455/) (2026)

## Abstract (arXiv)

Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past decisions and reproduce unfair decisions. In this paper, we propose two algorithms that adjust fitted ML predictors to make them fair. We focus on two legal notions of fairness: (a) providing equal opportunity (EO) to individuals regardless of sensitive attributes and (b) repairing historical disadvantages through affirmative action (AA). More technically, we produce fair EO and AA predictors by positing a causal model and considering counterfactual decisions. We prove that the resulting predictors are theoretically optimal in predictive performance while satisfying fairness. We evaluate the algorithms, and the trade-offs between accuracy and fairness, on datasets about admissions, income, credit and recidivism.