Causal Structure Learning

Causal structure learning aims to discover cause-and-effect relationships between variables from data, often using observational and interventional data to overcome limitations of purely observational studies. Current research emphasizes developing robust algorithms that handle unobserved variables, measurement error, and nonlinear relationships, often employing constraint-based methods, score-based methods, or increasingly, deep learning models like generative adversarial networks (GANs) and graph neural networks. These advancements are crucial for improving the reliability and interpretability of causal inferences across diverse fields, including healthcare, biology, and social sciences, enabling more effective decision-making and scientific discovery.

Papers

June 15, 2023

Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, Ranveer Chandra
Representation Learning Causal Discovery Method Causal Structure Learning Soil Process

June 12, 2023

Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge
Lyuzhou Chen, Taiyu Ban, Xiangyu Wang, Derui Lyu, Huanhuan Chen
Large Language Model Causal Structure Causal Relationship Prior Knowledge Causal Structure Learning Prior Correction

May 31, 2023

Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning
Deniz Koyuncu, Alex Gittens, Bülent Yener, Moti Yung
Adversarial Attack Causal Structure Causal Model Causal Structure Learning Causal Machine Learning Missingness Shift Omission Detection

May 10, 2023

May 5, 2023

Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu, Kiattikun Chobtham, Arian Hashemzadeh, Praharsh A. Nanavati, Rendani Mbuvha, Bruno Petrungaro
Case Study Covid 19 Causal Representation Open Problem Cause Effect Causal Structure Learning Causal Machine Learning United Kingdom

April 6, 2023

Efficient SAGE Estimation via Causal Structure Learning
Christoph Luther, Gunnar König, Moritz Grosse-Wentrup
Causal Structure Shapley Value Separation Performance Causal Structure Learning Importance Estimation

January 29, 2023

On Learning Necessary and Sufficient Causal Graphs
Hengrui Cai, Yixin Wang, Michael Jordan, Rui Song
LeArning Abstract Causal Graph Causal Structure Learning Causal Feature

January 26, 2023

Causal Structural Learning from Time Series: A Convex Optimization Approach
Song Wei, Yao Xie
Time Series Convex Optimization Structure Learning Causal Structure Learning Adaptive Linear

January 4, 2023

November 22, 2022

Reinforcement Causal Structure Learning on Order Graph
Dezhi Yang, Guoxian Yu, Jun Wang, Zhengtian Wu, Maozu Guo
Causal Discovery Causal Graph Causal Structure Learning Order Graph Acyclic Graph

November 13, 2022

Towards Privacy-Aware Causal Structure Learning in Federated Setting
Jianli Huang, Xianjie Guo, Kui Yu, Fuyuan Cao, Jiye Liang
Federated Learning Privacy Preserving Causal Structure Federated Setting Causal Structure Learning

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

August 14, 2022

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash
Constructive Approach Speech Presence Long Form Novel Structure Learning Causal Structure Learning Unobserved Variable Markov Equivalence Class Causal Order Ancestral Graph

June 11, 2022

Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
Kiattikun Chobtham, Anthony C. Constantinou
Scientific Discovery Evidence Piece Bayesian Network Density Estimation Expectation Maximization Causal Structure Learning Latent Confounders Variational Bayesian

June 3, 2022

Causality Learning With Wasserstein Generative Adversarial Networks
Hristo Petkov, Colin Hanley, Feng Dong
Causal Structure Directed Acyclic Graph Causal Structure Learning Wasserstein Generative Adversarial Network Causality Based Learning

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 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

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