# Counterfactual Fairness with Graph Uncertainty

arXiv: https://arxiv.org/abs/2601.03203 · HTML: https://mlsanity.com/paper/2601.03203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees](https://mlsanity.com/paper/2310.17679/) (2023)
- [The Sensitivity of Counterfactual Fairness to Unmeasured Confounding](https://mlsanity.com/paper/1907.01040/) (2019)
- [Counterfactual Fairness with Partially Known Causal Graph](https://mlsanity.com/paper/2205.13972/) (2022)
- [Causal Discovery for Fairness](https://mlsanity.com/paper/2206.06685/) (2022)
- [How to be fair? A study of label and selection bias](https://mlsanity.com/paper/2403.14282/) (2024)

## Built on by (most cited first, 1 of 1)

- [Implementing Causal Perception: Competing SCMs and Situated Fairness](https://mlsanity.com/paper/2608.03917/) (2026)

## Abstract (arXiv)

Evaluating machine learning (ML) model bias is key to building trustworthy and robust ML systems. Counterfactual Fairness (CF) audits allow the measurement of bias of ML models with a causal framework, yet their conclusions rely on a single causal graph that is rarely known with certainty in real-world scenarios. We propose CF with Graph Uncertainty (CF-GU), a bias evaluation procedure that incorporates the uncertainty of specifying a causal graph into CF. CF-GU (i) bootstraps a Causal Discovery algorithm under domain knowledge constraints to produce a bag of plausible Directed Acyclic Graphs (DAGs), (ii) quantifies graph uncertainty with the normalized Shannon entropy, and (iii) provides confidence bounds on CF metrics. Experiments on synthetic data show how contrasting domain knowledge assumptions support or refute audits of CF, while experiments on real-world data (COMPAS and Adult datasets) pinpoint well-known biases with high confidence, even when supplied with minimal domain knowledge constraints.