Causal Learning

Causal learning aims to uncover cause-and-effect relationships within data, moving beyond mere correlation to understand how interventions affect outcomes. Current research emphasizes developing algorithms and models, including those based on graph neural networks, to learn causal structures from observational data, often addressing challenges like confounding variables and missing data. This field is crucial for improving the reliability and interpretability of machine learning models across diverse applications, from improving industrial troubleshooting to enhancing personalized medicine and creating more robust AI systems. The development of standardized benchmarks and open-source tools is also a significant focus, facilitating collaborative research and reproducible results.

Papers

November 6, 2024

ION-C: Integration of Overlapping Networks via Constraints
Praveen Nair, Payal Bhandari, Mohammadsajad Abavisani, Sergey Plis, David Danks
Ground Truth Participation Constraint Causal Learning Novel Algorithm Independence Testing

November 1, 2024

Towards Robust Text Classification: Mitigating Spurious Correlations with Causal Learning
Yuqing Zhou, Ziwei Zhu
Feature Selection Spurious Correlation Causal Learning Robust Classifier Causal Feature Inverse Propensity Weighting Robust Text Classification

October 25, 2024

LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
Yue Cheng, Jiajun Zhang, Weiwei Xing, Xiaoyu Guo, Xiaohui Gao
Time Series Data Causal Learning Dynamic Graph Learning Causal System Rotation Matrix

October 21, 2024

ExDBN: Exact learning of Dynamic Bayesian Networks
Pavel Rytíř, Aleš Wodecki, Georgios Korpas, Jakub Mareček
Causal Relationship Bayesian Network Causal Learning Score Based Dynamic Bayesian Network Acyclic Graph Exact Learning

October 10, 2024

Learning Low-Level Causal Relations using a Simulated Robotic Arm
Miroslav Cibula, Matthias Kerzel, Igor Farkaš
Causal Effect Causal Relation Causal Learning Intelligent Robot Sensorimotor Control

October 4, 2024

Learning Truncated Causal History Model for Video Restoration
Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Di Niu
Video Deblurring Causal Learning Video Restoration Image Desnowing

October 2, 2024

Efficient Long-range Language Modeling with Self-supervised Causal Retrieval
Xiang Hu, Zhihao Teng, Wei Wu, Kewei Tu
Language Model Long Context Causal Learning Causal Language Long Range Language

September 12, 2024

Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
Ahmet Kapkiç, Pratanu Mandal, Shu Wan, Paras Sheth, Abhinav Gorantla, Yoonhyuk Choi, Huan Liu, K. Selçuk Candan
Machine Learning Causal Relationship Causal Direction Causal Analysis Causal Learning Benchmark Framework CausalBench Challenge

August 14, 2024

A General Framework for Constraint-based Causal Learning
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
Causal Discovery General Framework Sparse Representation Causal Learning

July 30, 2024

Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept
Alexandre Trilla, Ossee Yiboe, Nenad Mijatovic, Jordi Vitrià
Causal Relationship High Impact Concept Causal Learning Predictive Maintenance Partial Proof Causal Approach Causal Explanation Causal Machine Intelligent Fault Diagnosis

July 1, 2024

Improve ROI with Causal Learning and Conformal Prediction
Meng Ai, Zhuo Chen, Jibin Wang, Jing Shang, Tao Tao, Zhen Li
Conformal Prediction Causal Learning Treatment Assignment Direct Roi Prediction

June 21, 2024

June 15, 2024

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang
Causal Graph Speech Presence Extended Version Causal Learning Latent Confounders Human Skeleton Differentiable Causal Discovery

April 22, 2024

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
Kang Luo, Yuanshao Zhu, Wei Chen, Kun Wang, Zhengyang Zhou, Sijie Ruan, Yuxuan Liang
Unobserved Confounders Causal Learning Trajectory Model Trajectory Classification Trajectory Representation Learning

April 16, 2024

Vision-and-Language Navigation via Causal Learning
Liuyi Wang, Zongtao He, Ronghao Dang, Mengjiao Shen, Chengju Liu, Qijun Chen
Vision and Language Navigation Cross Modal Representation Causal Learning Modal Feature Causal Transformer

April 9, 2024

CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Beicheng Huang, Jibin Wu, Liang Feng, Kay Chen Tan
Comprehensive Benchmark Causal Learning Causal Interpretation Causal Network Causality Analysis CausalBench Challenge

March 7, 2024

Why Online Reinforcement Learning is Causal
Oliver Schulte, Pascal Poupart
Reinforcement Learning Causal Model Causal Relationship Online Reinforcement Learning Causal Learning Causal Event

February 23, 2024

Learning Cyclic Causal Models from Incomplete Data
Muralikrishnna G. Sethuraman, Faramarz Fekri
Causal Graph Causal Learning Incomplete Data Causal Discovery Algorithm Causal Dynamic Learning

February 20, 2024

Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition
Yuke Li, Guangyi Chen, Ben Abramowitz, Stefano Anzellott, Donglai Wei
Action Recognition Causal Learning Shot Action Recognition Temporal Shift Invariant Causal Feature