Causal Explanation

Causal explanation in machine learning aims to move beyond simple correlations, providing understandable and actionable reasons for model predictions. Current research focuses on developing methods that identify causal relationships within data, often leveraging techniques from causal inference, graph neural networks, and large language models to generate explanations in various forms, including counterfactuals and structural causal models. This work is crucial for building trust in AI systems, improving model fairness and robustness, and facilitating effective human-AI collaboration across diverse applications like healthcare, industrial maintenance, and autonomous driving.

Papers

May 30, 2022

Causal Explanations for Sequential Decision Making Under Uncertainty
Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman, Shlomo Zilberstein
Markov Decision Process Causal Inference High Uncertainty Anticipation Structural Causal Model Sequential Decision Making Causal Explanation

April 23, 2022

Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua
Graph Neural Network Feature Attribution Subgraph Explanation Causal Explanation

April 21, 2022

Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI
Greta Warren, Mark T Keane, Ruth M J Byrne
Inherent Interpretability High Explainability Counterfactual Explanation Feature Wise xAI Community Causal Explanation Subjective Evaluation Counterfactual Method

April 9, 2022

Why did I fail? A Causal-based Method to Find Explanations for Robot Failures
Maximilian Diehl, Karinne Ramirez-Amaro
Line by Line Explanation Causal Model Causal Approach Causal Explanation Robot Failure Causal Bayesian Network Failure Explanation

March 29, 2022

OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
Wanyu Lin, Hao Lan, Hao Wang, Baochun Li
Causal Graph Explanation Generation Latent Causal Causal Explanation Subgraph Explanation Interpretable Graph Neural Network Causal Hierarchy

January 31, 2022

Causal Explanations and XAI
Sander Beckers
Counterfactual Explanation Causal Model xAI Community Causal Explanation Causal View

December 17, 2021

Interpretable Data-Based Explanations for Fairness Debugging
Romila Pradhan, Jiongli Zhu, Boris Glavic, Babak Salimi
Model Bias Fairness Metric eXplainable Artificial Intelligence Achieving Fairness Causal Explanation

September 24, 2021

Optimization-based Causal Estimation from Heterogenous Environments
Mingzhang Yin, Yixin Wang, David M. Blei
Causal Inference Causal Model Causal Relationship Causal Effect Estimation Causal Explanation Heterogeneity Environment