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

June 17, 2022

June 14, 2022

The Causal Structure of Semantic Ambiguities
Daphne Wang, Mehrnoosh Sadrzadeh
Causal Structure Psycholinguistic Research Structural Ambiguity Additional Disambiguation Task Semantic Ambiguity Disambiguation Process

June 13, 2022

Invariant Structure Learning for Better Generalization and Causal Explainability
Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
Causal Structure Better Generalization Invariant Causal Causal Structure Discovery

June 12, 2022

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network
Zongyuan Huang, Shengyuan Xu, Menghan Wang, Hansi Wu, Yanyan Xu, Yaohui Jin
Causal Structure Causal Event Next Location Prediction Human Mobility Prediction

June 9, 2022

On the Generalization and Adaption Performance of Causal Models
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke
Strong Generalization Causal Structure Neural Causal Model Differentiable Causal Discovery AdaPtation Performance Better Out of Distribution Generalization

June 4, 2022

June 3, 2022

June 2, 2022

Causal Structure Learning: a Combinatorial Perspective
Chandler Squires, Caroline Uhler
Causal Discovery Causal Graph Causal Structure Causal Structure Learning Combinatorial Approach

May 31, 2022

Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua
Causal Discovery Causal Structure Differentiable Causal Discovery

May 25, 2022

Amortized Inference for Causal Structure Learning
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf
Variational Inference Causal Discovery Causal Structure Structure Learning Causal Structure Learning Amortized Inference

May 19, 2022

May 5, 2022

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
Financial Application Causal Structure Polynomial Time Automatic Counting Causal Effect Identification Markov Equivalence Class Causal Graphical Model

April 11, 2022

April 1, 2022

DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks
Hristo Petkov, Colin Hanley, Feng Dong
Generative Model Causal Structure Causal Structure Learning Wasserstein Generative Adversarial Network

March 30, 2022

Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen
Variational Autoencoders Causal Structure Causal Representation Learning Latent Causal

Causal Structure

Papers

The Impact of Variable Ordering on Bayesian Network Structure Learning

Reframed GES with a Neural Conditional Dependence Measure

The Causal Structure of Semantic Ambiguities

Invariant Structure Learning for Better Generalization and Causal Explainability

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

On the Generalization and Adaption Performance of Causal Models

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Combinatorial Causal Bandits

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Causality Learning With Wasserstein Generative Adversarial Networks

Causal Structure Learning: a Combinatorial Perspective

Differentiable Invariant Causal Discovery

Amortized Inference for Causal Structure Learning

Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)

Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data

Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge

Learning to Induce Causal Structure

DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks

Weakly supervised causal representation learning