Causal Fairness

Causal fairness in machine learning aims to develop algorithms that make unbiased decisions by explicitly modeling and mitigating the causal relationships between sensitive attributes (e.g., race, gender), predictor variables, and the outcome. Current research focuses on developing methods that leverage causal graphs and interventional approaches to identify and remove discriminatory causal pathways, often employing neural networks or constrained optimization techniques for fair prediction. This field is crucial for ensuring fairness and equity in AI systems across various applications, from loan applications to healthcare, by moving beyond simple statistical correlations to address the root causes of bias.

Papers

August 17, 2024

Fairness-Aware Streaming Feature Selection with Causal Graphs
Leizhen Zhang, Lusi Li, Di Wu, Sheng Chen, Yi He
Feature Selection Causal Graph Feature Correlation Causal Fairness Fair Feature Selection

May 24, 2024

Fairness-Accuracy Trade-Offs: A Causal Perspective
Drago Plecko, Elias Bareinboim
Fairness Constraint Fairness Accuracy Causal Fairness

May 23, 2024

Local Causal Discovery for Structural Evidence of Direct Discrimination
Jacqueline Maasch, Kyra Gan, Violet Chen, Agni Orfanoudaki, Nil-Jana Akpinar, Fei Wang
Causal Discovery Structural Causal Model Causal Relation Causal Fairness Direct Discrimination Local Causal Discovery CIViC Evidence

March 30, 2024

Addressing Both Statistical and Causal Gender Fairness in NLP Models
Hannah Chen, Yangfeng Ji, David Evans
NLP Model Fairness Notion Bias Metric Counterfactual Data Augmentation Causal Fairness Statistical Fairness Gender Fairness

January 19, 2024

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Aoqi Zuo, Yiqing Li, Susan Wei, Mingming Gong
Causal Inference Causal Graph Constrained Optimization Fairness Intervention Causal Fairness Causal DAG Fair Prediction

December 14, 2023

Fair Clustering: A Causal Perspective
Fritz Bayer, Drago Plecko, Niko Beerenwinkel, Jack Kuipers
Fairness Notion Fair Clustering Causal Fairness

November 30, 2023

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
Maresa Schröder, Dennis Frauen, Stefan Feuerriegel
Biased Prediction Fairness Method Unmeasured Confounding Causal Fairness Fair Prediction Sensitivity Sampling

November 17, 2023

Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri, Gjergji Kasneci
Neural Network Causal Graph Causal Model Causal Event Causal Fairness Data Reweighting

November 15, 2023

Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis
David F. Jenny, Yann Billeter, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin
Language Model Absolute Stance Bias Bias Mitigation Argument Quality LLM Bias Dependency Structure Causal Fairness Open Environment

October 30, 2023

Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Adversarial Robustness Adversarial Perturbation Causal Structure Structural Causal Model Individual Fairness Causal Fairness

June 19, 2023

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu
Causal Graph Causal Effect Causal Effect Estimation Cause Effect Graph Knowledge Markov Equivalence Class Causal Fairness Front Door Adjustment

June 8, 2023

Causal Fairness for Outcome Control
Drago Plecko, Elias Bareinboim
Causal Fairness Causal Intervention Module

November 21, 2022

Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak
Electronic Health Record Fairness Metric Fairness Notion Treatment Assignment Causal Fairness

July 23, 2022

Causal Fairness Analysis
Drago Plecko, Elias Bareinboim
Causal Fairness Causal Assumption

July 12, 2022

Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann Gaebler, Ravi Shroff, Sharad Goel
Procedural Fairness Algorithmic Fairness Fair Algorithm Experiential Consequence Causal Fairness Causal Concept

February 28, 2022

Selection, Ignorability and Challenges With Causal Fairness
Jake Fawkes, Robin Evans, Dino Sejdinovic
Technical Challenge Causal Model View Selection Counterfactual Prediction Counterfactual Fairness Causal Fairness Causal Assumption

January 26, 2022

Promises and Challenges of Causality for Ethical Machine Learning
Aida Rahmattalabi, Alice Xiang
Technical Challenge Causal Inference Causal Pattern Promise Responsible Machine Learning Causal Fairness