Structural Causal

Structural causal modeling aims to learn and represent causal relationships within data, enabling counterfactual reasoning and improved decision-making. Current research focuses on developing algorithms for causal discovery from observational data, often employing deep learning architectures like transformers and incorporating techniques from optimal transport and integer linear programming to handle high-dimensional data and complex causal structures. These advancements are improving the accuracy and efficiency of causal inference across diverse fields, including traffic prediction, recommendation systems, and manufacturing process optimization, leading to more robust and interpretable models.

Papers

November 8, 2024

Discovering Latent Structural Causal Models from Spatio-Temporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yi-An Ma, Rose Yu
Variational Inference Latent Variable Temporal Data Temporal Latent Structural Causal Temporal Spatial Causal

November 2, 2024

Hierarchical and Density-based Causal Clustering
Kwangho Kim, Jisu Kim, Larry A. Wasserman, Edward H. Kennedy
K Mean Heterogeneous Treatment Effect Structural Causal Regression Function

October 24, 2024

MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
Muralikrishnna G. Sethuraman, Razieh Nabi, Faramarz Fekri
Raw Data Causal Discovery Biological Network Uncertainty Induced Incomplete Structural Causal Missingness Shift

October 13, 2024

Online Multi-modal Root Cause Analysis
Lecheng Zheng, Zhengzhang Chen, Haifeng Chen, Jingrui He
Causal Structure Causal Direction Root Cause Structural Causal

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

July 19, 2024

MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
Shuang Li, Ziyuan Pu, Nan Zhang, Duxin Chen, Lu Dong, Daniel J. Graham, Yinhai Wang
Time Varying Counterfactual Prediction Traffic State Estimation Structural Causal

June 15, 2024

DCDILP: a distributed learning method for large-scale causal structure learning
Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi
Causal Discovery Causal Graph Causal Relationship Divide and Conquer Learning Method Structural Causal

May 8, 2024

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Michèle Sébag, Marc Schoenauer
Technical Challenge NCD Method Causal Structure Structural Causal Model Formal Guarantee Structural Causal Counterfactual Query Deep Structural Causal

April 21, 2024

Inference of Causal Networks using a Topological Threshold
Filipe Barroso, Diogo Gomes, Gareth J. Baxter
Scientific Inference Causal Pattern Causal Network Structural Causal Causal DAG

December 10, 2023

Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Tianqianjin Lin, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Weikang Yuan, Xurui Li, Changlong Sun, Cui Huang, Xiaozhong Liu
Heterogeneous Graph Structural Causal Model Human Perception Underlying Causal Structural Causal

October 30, 2023

A General Neural Causal Model for Interactive Recommendation
Jialin Liu, Xinyan Su, Peng Zhou, Xiangyu Zhao, Jun Li
Personalized Recommendation Counterfactual Inference Structural Causal Neural Causal Model

July 31, 2023

A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur, Felix Laumann, Junhyung Park, Mauricio Barahona
Causal Inference Causal Graph Structural Causal Hamming Distance Hierarchical Similarity

May 26, 2023

Learning Causal Graphs via Monotone Triangular Transport Maps
Sina Akbari, Luca Ganassali, Negar Kiyavash
Causal Discovery Causal Graph Causal Structure Structural Causal Transport Map Based Causal Discovery

February 26, 2023

Q-Cogni: An Integrated Causal Reinforcement Learning Framework
Cris Cunha, Wei Liu, Tim French, Ajmal Mian
Reinforcement Learning State of the Art Reinforcement Structural Causal Causal Reinforcement Learning

January 22, 2023

Counterfactual (Non-)identifiability of Learned Structural Causal Models
Arash Nasr-Esfahany, Emre Kiciman
Causal Structure Structural Causal Model Counterfactual Inference Structural Causal Causal Identifiability Deep Structural Causal Non Identifiability

November 25, 2022

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi, Michèle Sebag
Causal Structure Functional Magnetic Resonance Imaging Structural Equation Structural Causal Matrix Decomposition Sparse Decomposition

October 26, 2022

Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Maximilian Kertel, Stefan Harmeling, Markus Pauly
Cause Effect Structural Equation Structural Causal Process Control

October 24, 2022

Learning Latent Structural Causal Models
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
Causal Model Causal Learning Underlying Causal Structural Causal

July 14, 2022

Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
Samuel C. Hoffman, Payel Das, Karthikeyan Shanmugam, Kahini Wadhawan, Prasanna Sattigeri
Generative Model Causal Graph Limited Data Root to Leaf Path Structural Causal Generative Autoencoder

May 19, 2022

Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)
Fabrizio Russo, Francesca Toni
Causal Discovery Causal Graph Causal Structure Causal Relationship Document Relevance Knowledge Injection Structural Causal