Causal DAG

Causal Directed Acyclic Graphs (DAGs) represent causal relationships between variables, aiming to infer the direction of influence from observational or interventional data. Current research focuses on developing efficient algorithms, such as constraint-based methods (e.g., PC algorithm) and those leveraging linear non-Gaussian models (e.g., LiNGAM), to learn these DAGs, often incorporating techniques like GPU acceleration to handle large datasets and addressing challenges posed by heteroscedastic noise and partially known graphs. This field is crucial for advancing causal inference in various domains, enabling more accurate causal effect estimation and fairer machine learning models by accounting for confounding biases and utilizing background knowledge.

Papers

August 31, 2024

Learning linear acyclic causal model including Gaussian noise using ancestral relationships
Ming Cai, Penggang Gao, Hisayuki Hara
Causal Model Structural Causal Model Gaussian Noise Causal DAG Distributional Invariance

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

March 6, 2024

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Victor Akinwande, J. Zico Kolter
Causal Discovery Single GPU Speed Effect Causal Discovery Method Large Scale Datasets Gene Expression Data Causal Ordering Causal DAG

January 19, 2024

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Aoqi Zuo, Yiqing Li, Susan Wei, Mingming Gong
Causal Inference Causal Graph Constrained Optimization Fairness Intervention Causal Fairness Causal DAG Fair Prediction

December 20, 2023

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
Naiyu Yin, Tian Gao, Yue Yu, Qiang Ji
Causal Discovery Directed Acyclic Graph Causal DAG Heteroscedastic Noise Asymmetry Labeled DAG

November 17, 2023

Concept-free Causal Disentanglement with Variational Graph Auto-Encoder
Jingyun Feng, Lin Zhang, Lili Yang
Causal Disentanglement Causal DAG Variational Graph Auto Meta Tree

October 6, 2023

A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton
Vidya Sagar Sharma
Human Skeleton Markov Equivalence Class Causal DAG Fixed Parameter

July 16, 2023

Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Causal Ordering Causal DAG Temporal Causal

May 8, 2023

New metrics and search algorithms for weighted causal DAGs
Davin Choo, Kirankumar Shiragur
Causal Graph Causal Relationship Parameter Update Search Algorithm New Metric Causal DAG Node Intervention

November 15, 2022

Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Jia Li, Xiang Li, Xiaowei Jia, Michael Steinbach, Vipin Kumar
Latent Space Causal Model Causal Representation Learning Causal Event Causal Representation Confounding Bias Causal DAG Asymmetry Labeled DAG

October 29, 2022

Causal DAG extraction from a library of books or videos/movies
Robert R. Tucci
Artificial Intelligence Causal Inference Gameplay Video Easy to Use Library Movie Review Book Review Causality Extraction Causal DAG Acyclic Graph

July 10, 2022

On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He
Financial Application Causal Inference Individual Representation Directed Acyclic Graph Causal Knowledge Causal DAG Explicitly Mentioned Causal Fact

June 30, 2022

Verification and search algorithms for causal DAGs
Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya
Causal Graph Verification Task Search Algorithm Causal DAG Ancestral Graph

May 27, 2022

November 9, 2021

Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
Vibhor Porwal, Piyush Srivastava, Gaurav Sinha
Lower Bound Synthetic Graph Markov Equivalence Class Causal DAG Node Intervention