# Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

arXiv: https://arxiv.org/abs/1901.10501 · HTML: https://mlsanity.com/paper/1901.10501/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 30 of 48)

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- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
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- [Fairness via Representation Neutralization](https://mlsanity.com/paper/2106.12674/) (2021)
- [LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination](https://mlsanity.com/paper/2309.11696/) (2023)
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- [Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives](https://mlsanity.com/paper/2307.16851/) (2023)
- [Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime](https://mlsanity.com/paper/2112.08297/) (2021)
- [Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting](https://mlsanity.com/paper/2212.06803/) (2022)
- [Understanding Influence Functions and Datamodels via Harmonic Analysis](https://mlsanity.com/paper/2210.01072/) (2022)
- [Editable Concept Bottleneck Models](https://mlsanity.com/paper/2405.15476/) (2024)
- [Improving Recommendation Fairness via Data Augmentation](https://mlsanity.com/paper/2302.06333/) (2023)
- [LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions](https://mlsanity.com/paper/2505.23811/) (2025)
- [Fairness with Continuous Optimal Transport](https://mlsanity.com/paper/2101.02084/) (2021)
- [FAIR-FATE: Fair Federated Learning with Momentum](https://mlsanity.com/paper/2209.13678/) (2022)
- [Enabling SQL-based Training Data Debugging for Federated Learning](https://mlsanity.com/paper/2108.11884/) (2021)
- [Dissecting Representation Misalignment in Contrastive Learning via Influence Function](https://mlsanity.com/paper/2411.11667/) (2024)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation](https://mlsanity.com/paper/2410.05090/) (2024)
- [Can Information Flows Suggest Targets for Interventions in Neural Circuits?](https://mlsanity.com/paper/2111.05299/) (2021)
- [When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction](https://mlsanity.com/paper/2206.02058/) (2022)
- [Impact of Data Processing on Fairness in Supervised Learning](https://mlsanity.com/paper/2102.01867/) (2021)
- [Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing](https://mlsanity.com/paper/2501.06366/) (2025)
- [Information Theoretic Measures for Fairness-aware Feature Selection](https://mlsanity.com/paper/2106.00772/) (2021)
- [A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission](https://mlsanity.com/paper/2206.06279/) (2022)
- [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023)
- [An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty](https://mlsanity.com/paper/2212.06828/) (2022)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Unraveling Indirect In-Context Learning Using Influence Functions](https://mlsanity.com/paper/2501.01473/) (2025)

## Abstract (arXiv)

When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group. In this paper, we exploit this fact to reduce the disparate impact of a fixed classification model over a population of interest. Given a black-box classifier, we aim to eliminate the performance gap by perturbing the distribution of input variables for the disadvantaged group. We refer to the perturbed distribution as a counterfactual distribution, and characterize its properties for common fairness criteria. We introduce a descent algorithm to learn a counterfactual distribution from data. We then discuss how the estimated distribution can be used to build a data preprocessor that can reduce disparate impact without training a new model. We validate our approach through experiments on real-world datasets, showing that it can repair different forms of disparity without a significant drop in accuracy.