Causal Structure

Causal structure research aims to uncover cause-and-effect relationships within complex systems, using both observational and interventional data to build accurate causal models. Current research focuses on developing robust algorithms for causal discovery, including those leveraging graph neural networks, score-matching techniques, and reinforcement learning to learn directed acyclic graphs (DAGs) efficiently, even in the presence of latent variables and non-linear relationships. These advancements are crucial for improving the reliability and interpretability of machine learning models across diverse fields, from healthcare and robotics to climate science and marketing, enabling more effective decision-making based on a deeper understanding of causality.

Papers

November 7, 2022

Federated Causal Discovery From Interventions
Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou
Causal Discovery Causal Structure Heterogeneous Treatment Effect Causal Discovery Method Health Intervention Federated Causal

November 4, 2022

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin
Causal Structure Causal Model Functional Mechanism Energy Based GFlowNets Bayesian Learning Variational Bayes

November 3, 2022

Decomposing Counterfactual Explanations for Consequential Decision Making
Martin Pawelczyk, Lea Tiyavorabun, Gjergji Kasneci
Counterfactual Explanation Causal Structure Algorithmic Recourse Robust Recourse Causal Assumption Consequential Decision Making Causal Algorithmic Recourse

October 27, 2022

Grasping Causality for the Explanation of Criticality for Automated Driving
Tjark Koopmann, Christian Neurohr, Lina Putze, Lukas Westhofen, Roman Gansch, Ahmad Adee
Line by Line Explanation Causal Structure Automated Driving Causal Knowledge Causal Query Causal Interpretation Criticality Ordered Spin Sequence Causality Based Perspective

October 20, 2022

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Haoyue Dai, Peter Spirtes, Kun Zhang
Constructive Approach Causal Discovery Causal Graph Causal Structure Causal Model Local Independence Measurement Error

October 19, 2022

Causal Structure Learning with Recommendation System
Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang
Causal Structure Structural Causal Model Causal Structure Learning Causal System

October 15, 2022

GFlowCausal: Generative Flow Networks for Causal Discovery
Wenqian Li, Yinchuan Li, Shengyu Zhu, Yunfeng Shao, Jianye Hao, Yan Pang
Active Learning Causal Discovery Causal Structure Generative Flow Network High Reward DAG

October 10, 2022

Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
Fan Wu, Sanghyun Hong, Donsub Rim, Noseong Park, Kookjin Lee
Application Proficiency Causal Structure Continuous Time Model Actual Causality Continuous Time Dynamic Tsunami Scenario

October 6, 2022

Explanations as Programs in Probabilistic Logic Programming
Germán Vidal
Line by Line Explanation Causal Structure Logic Programming Past Present Temporal Program

September 28, 2022

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang
Causal Structure Unseen Graph Structural Bias Disentangled Causal

September 20, 2022

Fairness and robustness in anti-causal prediction
Maggie Makar, Alexander D'Amour
Native Robustness Procedural Fairness Causal Structure Causal Prediction Fairness Criterion Fairness Mitigation

September 15, 2022

Estimating large causal polytrees from small samples
Sourav Chatterjee, Mathukumalli Vidyasagar
Practical Algorithm Causal Structure Modeling Assumption Regulatory Network Single Sample

August 31, 2022

Learning Multiscale Non-stationary Causal Structures
Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi, Francesco Bonchi
Causal Graph Causal Effect Causal Structure Multivariate Time Series Data Causal Theory

August 15, 2022

The Causal Structure of Domain Invariant Supervised Representation Learning
Zihao Wang, Victor Veitch
Domain Shift Causal Structure Invariant Representation Domain Invariant Representation Model Invariance

July 18, 2022

A Meta-Reinforcement Learning Algorithm for Causal Discovery
Andreas Sauter, Erman Acar, Vincent François-Lavet
Causal Discovery Causal Graph Causal Structure Underlying Causal

July 16, 2022

Multiscale Causal Structure Learning
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
Causal Structure Causal Relationship

July 4, 2022

Invariant and Transportable Representations for Anti-Causal Domain Shifts
Yibo Jiang, Victor Veitch
Domain Shift Causal Structure Causal Direction Dynamical Invariant Causal Invariance