Causal Graphical Model

Causal graphical models represent causal relationships between variables as directed graphs, aiming to infer causal effects from observational data and guide interventions. Current research emphasizes developing robust algorithms for causal discovery and effect estimation, particularly within the context of complex scenarios like time series data, high-dimensional data with missing values (e.g., gene regulatory networks), and heterogeneous treatment effects. These advancements are improving the reliability and applicability of causal inference across diverse fields, from healthcare and robotics to machine learning and environmental science, by enabling more accurate modeling of complex systems and more effective decision-making under uncertainty.

Papers

July 2, 2024

Spatio-Temporal Graphical Counterfactuals: An Overview
Mingyu Kang, Duxin Chen, Ziyuan Pu, Jianxi Gao, Wenwu Yu
Structural Causal Model Counterfactual Analysis Causal Graphical Model Counterfactual Graph Counterfactual Model

March 21, 2024

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang
Speech Presence Structured Dropout Gene Expression Data Causal View Causal Graphical Model Transcriptomic Data Gene Regulatory Neural Network

March 3, 2024

A Human-Centered Approach for Bootstrapping Causal Graph Creation
Minh Q. Tram, Nolan B. Gutierrez, William J. Beksi
Causal Inference Causal Graph Causal Relationship Human Centric Causal Graphical Model

February 7, 2024

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao
LeArning Abstract Causal Structure Causal Structure Learning Causal Graphical Model Causal Reinforcement Learning

October 9, 2023

Projecting infinite time series graphs to finite marginal graphs using number theory
Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge
Causal Discovery Causal Graph Projection Bias Causal Graphical Model Number Theory Complex Posterior

September 19, 2023

Partially Specified Causal Simulations
A. Zamanian, L. Mareis, N. Ahmidi
Causal Inference Causal Graphical Model Novel Causal Model

August 2, 2023

CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models
Robert Maier, Andreas Schlattl, Thomas Guess, Jürgen Mottok
Causal Model Causal Feature Causal Graphical Model Causality Analysis

July 7, 2023

When does the ID algorithm fail?
Ilya Shpitser
Causal Model Dynamic Feature Input Distribution Causal Graphical Model Interventional Distribution

July 6, 2023

Sparse Graphical Linear Dynamical Systems
Emilie Chouzenoux, Victor Elvira
Granger Causality Linear System Causal Graphical Model Graphical Lasso

June 30, 2023

Redeeming Data Science by Decision Modelling
John Mark Agosta, Robert Horton
Machine Learning Model Data Science Causal Graphical Model Decision Making Model

January 29, 2023

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
Richard A Watson, Hengrui Cai, Xinming An, Samuel McLean, Rui Song
Causal Pattern Heterogeneous Treatment Effect Causal Graphical Model Heterogeneous Causal

December 8, 2022

Optimizing Integrated Information with a Prior Guided Random Search Algorithm
Eduardo C. Garrido-Merchán, Javier Sánchez-Cañizares
Human Consciousness Information Theoretic Framework Causal Graphical Model Random Search

August 26, 2022

Causal Bandits for Linear Structural Equation Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Cumulative Regret Structural Equation Causal Bandit Causal Graphical Model

August 20, 2022

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le
Timely Survey Causal Graph Causal Effect Causal Effect Estimation Causal Graphical Model

June 22, 2022

Variational Causal Dynamics: Discovering Modular World Models from Interventions
Anson Lei, Bernhard Schölkopf, Ingmar Posner
Variational Inference Causal Discovery World Model Health Intervention Causal Graphical Model Variational Causal Latent World Model

June 14, 2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing
Causal Inference Causal Graph Extension Study Causal Query Causal Graphical Model Visual Causality Discovery

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