Causal Concept

Causal concept research aims to understand and model causal relationships within data, moving beyond simple correlations to enable more reliable and interpretable AI systems. Current efforts focus on developing models that explicitly represent causal structures, such as Causal Concept Embedding Models and diffusion-based generative models for time series, allowing for counterfactual analysis and interventional reasoning. This work is crucial for addressing issues like bias amplification in decision-making algorithms and improving the trustworthiness and fairness of AI, with implications across various fields including healthcare, social sciences, and engineering.

Papers

November 14, 2024

MCCE: Missingness-aware Causal Concept Explainer
Jifan Gao, Guanhua Chen
Causal Effect Estimation Unobserved Node Causal Concept

May 26, 2024

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Deep Learning Full Model Causal Model Ground Glass Opacity Interpretable Causal Causal Concept

May 24, 2024

May 12, 2024

Probabilistic and Causal Satisfiability: the Impact of Marginalization
Julian Dörfler, Benito van der Zander, Markus Bläser, Maciej Liskiewicz
Global Impact Probabilistic Model Counterfactual State Causal Concept Causal Hierarchy

February 5, 2024

Counterfactual Fairness Is Not Demographic Parity, and Other Observations
Ricardo Silva
Counterfactual Fairness Demographic Parity Causal Concept

November 2, 2023

A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations
Hang Chen, Keqing Du, Chenguang Li, Xinyu Yang
Meaningful Representation Causal Structure Causal Relationship Requirement Based Roadmap Deep Causal Causal Concept

July 15, 2023

Causal Laws and Multi-Valued Fluents
Enrico Giunchiglia, Joohyung Lee, Vladimir Lifschitz, Hudson Turner
Non Axiomatic Reasoning Causal Assumption Causal Concept Language Action Causal Theory

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

May 12, 2022

Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?
Marko Tesic, Ulrike Hahn
Human Prediction High Explainability Counterfactual Explanation AI System Causal Relationship User Base Causal View Correlation Graph Causal Concept

April 4, 2022

Causality, Causal Discovery, and Causal Inference in Structural Engineering
M. Z. Naser
Causal Inference Causal Discovery Causal Model Causal Pattern Causal Approach Structural Engineering Causal Concept

February 2, 2022

Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts
Bo Zhang, Jiayao Zhang
Causal Inference Text Based Causal Query Parallel Text Human Subject Causal Concept