Underlying Causal

Underlying causal inference aims to uncover the true causal relationships between variables, moving beyond mere correlations to understand how changes in one variable directly affect others. Current research focuses on developing robust methods for causal discovery, particularly in high-dimensional and complex systems, employing techniques like Bayesian networks, neural networks, and optimal transport to learn causal structures from observational and interventional data. These advancements are crucial for improving the reliability and interpretability of machine learning models, enabling more effective decision-making in diverse fields such as healthcare, policy, and engineering.

Papers

October 25, 2024

Unified Causality Analysis Based on the Degrees of Freedom
András Telcs, Marcell T. Kurbucz, Antal Jakovác
Causal Relationship Latent Confounders Underlying Causal Different Degree Freedom Matter Causal Influence

October 1, 2024

Optimal Causal Representations and the Causal Information Bottleneck
Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen
Causal Structure Information Bottleneck Underlying Causal

August 29, 2024

Targeted Cause Discovery with Data-Driven Learning
Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho
Causal Relationship Causal Relation Causal Discovery Method Underlying Causal Causal Variable Data Driven Learning

August 1, 2024

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures
Alexandre Trilla, Nenad Mijatovic
Causal Discovery Mutual Information Stronger Generalizability Heterogeneous Data Underlying Causal Automatic Discovery Bivariate Observational Data

July 18, 2024

Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Data
Charles Jin, Martin Rinard
Language Model Raw Data NLP Task Structural Causal Model Linear Probing Underlying Causal Latent Concept

June 13, 2024

Investigating potential causes of Sepsis with Bayesian network structure learning
Bruno Petrungaro, Neville K. Kitson, Anthony C. Constantinou
Causal Inference Causal Structure Actual Cause Underlying Causal Bayesian Network Structure Standard Sepsis

April 16, 2024

Automated Discovery of Functional Actual Causes in Complex Environments
Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum
Reinforcement Learning Complex Environment Actual Cause Cause Effect Underlying Causal Automatic Discovery Actual Causality

March 13, 2024

A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
Causal Representation Learning Partial Observability Latent Causal Underlying Causal

February 23, 2024

Optimal Transport for Structure Learning Under Missing Data
Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
Optimal Transport Causal Discovery Causal Structure Structure Learning Causal Discovery Method Missing Data Underlying Causal

February 16, 2024

Robust agents learn causal world models
Jonathan Richens, Tom Everitt
Causal Inference Causal Model Underlying Causal Robust Agent Causal World Model

February 7, 2024

Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
Causal Representation Learning General Text Causal Representation Underlying Causal Multiple Distribution Parametric Causal

February 6, 2024

Discovery of the Hidden World with Large Language Models
Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
Scientific Discovery Causal Relation Causal Discovery Method Underlying Causal

February 1, 2024

Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Vincent Zhihao Zheng, Lijun Sun
Covariance Conditioning Underlying Causal Probabilistic Time Series Forecasting Correlated Noise Covariance Structure

December 28, 2023

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Offline Reinforcement Learning Underlying Causal Active Sampling

December 10, 2023

Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Tianqianjin Lin, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Weikang Yuan, Xurui Li, Changlong Sun, Cui Huang, Xiaozhong Liu
Heterogeneous Graph Structural Causal Model Human Perception Underlying Causal Structural Causal

November 10, 2023

Doubly Robust Structure Identification from Temporal Data
Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Stefan Bauer
Time Series Data Temporal Data Underlying Causal Confounding Bias Vector Regression

August 24, 2023

Inducing Causal Structure for Abstractive Text Summarization
Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng
Text Summarization Causal Structure Abstractive Text Summarization Underlying Causal

July 26, 2023

Causal reasoning in typical computer vision tasks
Kexuan Zhang, Qiyu Sun, Chaoqiang Zhao, Yang Tang
Causal Pattern Computer Vision Task Causal Reasoning Underlying Causal Explanatory Paradigm

July 19, 2023

Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo, Kexin Lou, Quanying Liu
Neural Network Evidence Piece EEG Signal Granger Causality Underlying Causal Effective Connectivity

June 8, 2023

A Causal Framework for Decomposing Spurious Variations
Drago Plecko, Elias Bareinboim
Causal Inference Causal Effect Underlying Causal Spurious Pattern Semi Markov