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

August 20, 2024

Asymmetric Graph Error Control with Low Complexity in Causal Bandits
Chen Peng, Di Zhang, Urbashi Mitra
Causal Graph Causal Structure Non Stationary Error Feedback Low Complexity Structural Causal Causal Bandit

August 15, 2024

CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning
Jinze Sun, Yongpan Sheng, Lirong He
Causal Structure Temporal Graph Temporal Knowledge Graph Causal Representation Learning Incomplete Knowledge Graph

August 9, 2024

July 26, 2024

July 24, 2024

On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
Latent Variable Causal Structure Structural Causal Model Parameter Identifiability

July 23, 2024

Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage
Lucas Possner, Lukas Bahr, Leonard Roehl, Christoph Wehner, Sophie Groeger
Causal Structure Root Cause Cause Effect Causal Discovery Algorithm

July 14, 2024

Optimal Kernel Choice for Score Function-based Causal Discovery
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
Causal Discovery Causal Structure Causal Relationship Kernel Selection Optimal Kernel Based Causal Discovery

July 12, 2024

Graph Neural Network Causal Explanation via Neural Causal Models
Arman Behnam, Binghui Wang
Graph Neural Network Causal Structure Structural Causal Model Causal Explanation Neural Causal Model GNN Explainers

July 10, 2024

Identifying macro conditional independencies and macro total effects in summary causal graphs with latent confounding
Simon Ferreira, Charles K. Assaad
Causal Inference Causal Graph Causal Structure Conditional Independence Causal Relation Intermediate Latent Complex Dynamical System

July 8, 2024

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Luka Kovačević, Izzy Newsham, Sach Mukherjee, John Whittaker
Causal Graph Causal Structure Causal Relationship Structural Causal Model Causal Structure Learning Causal Machine Simulation Benchmark Genetic Perturbation

July 4, 2024

Mechanisms for Data Sharing in Collaborative Causal Inference (Extended Version)
Björn Filter, Ralf Möller, Özgür Lütfü Özçep
Federated Learning Causal Inference Causal Structure Functional Mechanism Extended Version Data Sharing Collaborative Inference Cooperative Learning

July 1, 2024

June 29, 2024

Detecting and Identifying Selection Structure in Sequential Data
Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang
Data Detection Inductive Bias Causal Structure Sequential Data Latent Confounders Minimal Selection Principle

June 25, 2024

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
Carles Balsells-Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li
Causal Discovery Causal Structure Order Markov

June 24, 2024

CausalMMM: Learning Causal Structure for Marketing Mix Modeling
Chang Gong, Di Yao, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su, Jingping Bi
Causal Structure Interpretable Causal Marketing Mix Modeling

June 21, 2024

ExDAG: Exact learning of DAGs
Pavel Rytíř, Aleš Wodecki, Jakub Mareček
Causal Structure Directed Acyclic Graph Causal Learning Exact Learning

June 20, 2024

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel
Representation Learning Causal Structure Causal Representation Learning

Causal Structure

Papers

Asymmetric Graph Error Control with Low Complexity in Causal Bandits

CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees

Score matching through the roof: linear, nonlinear, and latent variables causal discovery

Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery

On the Parameter Identifiability of Partially Observed Linear Causal Models

Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage

Optimal Kernel Choice for Score Function-based Causal Discovery

Graph Neural Network Causal Explanation via Neural Causal Models

Identifying macro conditional independencies and macro total effects in summary causal graphs with latent confounding

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments

Mechanisms for Data Sharing in Collaborative Causal Inference (Extended Version)

Everything that can be learned about a causal structure with latent variables by observational and interventional probing schemes

Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals

Detecting and Identifying Selection Structure in Sequential Data

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

ExDAG: Exact learning of DAGs

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning