Causal Graph

Causal graphs are probabilistic graphical models used to represent causal relationships between variables, aiming to infer cause-and-effect structures from observational or interventional data. Current research focuses on developing algorithms for causal discovery, including constraint-based and score-based methods, often incorporating techniques like Bayesian inference, neural networks (e.g., convolutional neural networks), and reinforcement learning to improve scalability and accuracy, especially in high-dimensional datasets with missing data or latent confounders. These advancements have significant implications for various fields, enabling more robust and explainable AI systems, improved decision-making in complex systems (e.g., supply chains, healthcare), and deeper understanding of biological processes.

Papers

November 17, 2023

Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing
Zhengqi He, Taro Toyoizumi
Causal Graph Neural Language Model Human Brain Cortical Hierarchy Brain Prediction

November 13, 2023

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer
Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, Brian Piening, Kevin Matlock
Causal Discovery Causal Graph Causal Relationship Medical AI Lung Cancer Causal Structure Learning Dirichlet Prior

November 9, 2023

November 5, 2023

Causal Question Answering with Reinforcement Learning
Lukas Blübaum, Stefan Heindorf
Reinforcement Learning Causal Graph Causal Relationship Causal Relation Causal Query

November 2, 2023

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Hangtong Xu, Yuanbo Xu, Yongjian Yang
Latent Space Causal Graph Effective Recommendation Causal Structure Learning Text Based Confounders

October 28, 2023

Causal discovery in a complex industrial system: A time series benchmark
Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Time Series Causal Discovery Causal Graph Causal Structure Causal Discovery Method Industrial System Time Series Benchmark

October 26, 2023

Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf
Diffusion Model Causal Inference Causal Graph Causal Model Stochastic Differential Equation

October 24, 2023

Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
Franziska Heeg, Ingo Scholtes
Graph Neural Network Causal Graph Dynamic Graph Temporal Graph Node Centrality Betweenness Centrality Closeness Centrality

October 23, 2023

October 21, 2023

Specify Robust Causal Representation from Mixed Observations
Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang, Jun Wang
Representation Learning Causal Graph Mutual Information Causal Representation Mixed Speech

October 11, 2023

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi
Reinforcement Learning Causal Graph Posterior Sampling Bayesian Regret Causal System

October 10, 2023

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang
Large Language Model Code Generation Causal Graph Benchmark Platform Causal Approach LLM Generated Code Causality Analysis

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

October 5, 2023

CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu
Causal Discovery Causal Graph Causal Relationship New Way Causal Event Physical Information Video Reasoning

October 3, 2023

CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
Time Series Causal Discovery Causal Graph Benchmark Platform Causal Transformer Ground Truth Causal

September 30, 2023

Interpretable Imitation Learning with Dynamic Causal Relations
Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen
Imitation Learning Causal Graph Causal Relation Latent Causal

September 13, 2023

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Yuanhao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li
Causal Inference Causal Graph Causal Structure Causal Structure Learning Causal Approach

September 5, 2023

Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach
Mishfad Shaikh Veedu, James Melbourne, Murti V. Salapaka
Constructive Approach Causal Inference Causal Graph Causal Effect Cause Effect Linear Dynamical System Fast Fourier Transform

Causal Graph

Papers

Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

Diffusion Based Causal Representation Learning

Counterfactually Fair Representation

Causal Question Answering with Reinforcement Learning

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation

Causal discovery in a complex industrial system: A time series benchmark

Causal Modeling with Stationary Diffusions

Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

Causal Inference Using LLM-Guided Discovery

Identifiability of total effects from abstractions of time series causal graphs

Specify Robust Causal Representation from Mixed Observations

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach

Projecting infinite time series graphs to finite marginal graphs using number theory

CLEVRER-Humans: Describing Physical and Causal Events the Human Way

CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery

Interpretable Imitation Learning with Dynamic Causal Relations

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach