# Achieving Counterfactual Fairness with Imperfect Structural Causal Model

arXiv: https://arxiv.org/abs/2303.14665 · HTML: https://mlsanity.com/paper/2303.14665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved](https://mlsanity.com/paper/1811.11154/) (2018)
- [Two Simple Ways to Learn Individual Fairness Metrics from Data](https://mlsanity.com/paper/2006.11439/) (2020)
- [Average Individual Fairness: Algorithms, Generalization and Experiments](https://mlsanity.com/paper/1905.10607/) (2019)
- [The impossibility of "fairness": a generalized impossibility result for decisions](https://mlsanity.com/paper/1707.01195/) (2017)
- [Fairness Assessment for Artificial Intelligence in Financial Industry](https://mlsanity.com/paper/1912.07211/) (2019)
- [Kernel Wasserstein Distance](https://mlsanity.com/paper/1905.09314/) (2019)

## Built on by (most cited first, 2 of 2)

- [Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets](https://mlsanity.com/paper/2504.11504/) (2025)
- [Lookahead Counterfactual Fairness](https://mlsanity.com/paper/2412.01065/) (2024)

## Abstract (arXiv)

Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i.e., what if the individual belongs to other sensitive groups). The existing studies need to pre-define the structural causal model that captures the correlations among variables for counterfactual inference; however, the underlying causal model is usually unknown and difficult to be validated in real-world scenarios. Moreover, the misspecification of the causal model potentially leads to poor performance in model prediction and thus makes unfair decisions. In this research, we propose a novel minimax game-theoretic model for counterfactual fairness that can produce accurate results meanwhile achieve a counterfactually fair decision with the relaxation of strong assumptions of structural causal models. In addition, we also theoretically prove the error bound of the proposed minimax model. Empirical experiments on multiple real-world datasets illustrate our superior performance in both accuracy and fairness. Source code is available at urlhttps://github.com/tridungduong16/counterfactualfairnessgametheoretic.