Causal Model

Causal modeling aims to uncover cause-and-effect relationships within data, going beyond simple correlations to understand how variables influence each other. Current research focuses on developing algorithms and model architectures, such as Bayesian networks and linear structural equation models, that can effectively learn causal structures from observational and interventional data, even in the presence of hidden variables or confounding factors. This work is crucial for improving the reliability and interpretability of machine learning models across diverse fields, from robotics and healthcare to economics and climate science, by enabling more accurate predictions and informed decision-making under uncertainty. Furthermore, research is actively addressing challenges in model evaluation and the development of robust methods applicable to high-dimensional and complex data.

Papers

December 9, 2023

Mitigating Nonlinear Algorithmic Bias in Binary Classification
Wendy Hui, Wai Kwong Lau
Causal Model Bias Mitigation Binary Classification Algorithmic Bias Demographic Bias

December 6, 2023

Invariance & Causal Representation Learning: Prospects and Limitations
Simon Bing, Jonas Wahl, Urmi Ninad, Jakob Runge
Causal Model Fundamental Limitation Alpha Invariance Causal Representation Learning Visionary ProSpect Latent Causal Causal Representation Causal Variable

November 17, 2023

Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri, Gjergji Kasneci
Neural Network Causal Graph Causal Model Causal Event Causal Fairness Data Reweighting

November 15, 2023

Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans, Nicolette De Keizer, Kitty Jager, Ameen Abu-Hanna, Giovanni Cinà
Causal Inference Medical Diagnosis Causal Model Causal Direction Causal Prediction Causality Analysis Drug Safety Acute Kidney Injury

November 2, 2023

TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
Javier González, Cliff Wong, Zelalem Gero, Jass Bagga, Risa Ueno, Isabel Chien, Eduard Oravkin, Emre Kiciman, Aditya Nori, Roshanthi Weerasinghe, Rom S. Leidner, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon
Language Model Causal Model Clinical Trial Evidence Generation

October 27, 2023

Moral Responsibility for AI Systems
Sander Beckers
AI System Causal Model Epistemic State Moral Responsibility

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

October 23, 2023

October 22, 2023

Shortcuts for causal discovery of nonlinear models by score matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Francesco Locatello
Causal Discovery Causal Model Nonlinear Model Score Matching Shortcut Feature

October 19, 2023

Detecting and Mitigating Algorithmic Bias in Binary Classification using Causal Modeling
Wendy Hui, Wai Kwong Lau
Data Detection Gender Bias Causal Model Bias Mitigation Binary Classification Algorithmic Bias

October 17, 2023

September 18, 2023

September 8, 2023

Inferring physical laws by artificial intelligence based causal models
Jorawar Singh, Kishor Bharti, Arvind
Machine Learning Artificial Intelligence Causal Inference Causal Model Artificial General Intelligence Cause Effect Physical Law

August 30, 2023

Causal Strategic Learning with Competitive Selection
Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
Causal Model Selection Policy Sequential Selection Agent Selection Causal Strategic

August 24, 2023

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting
Causal Inference Causal Model Causal Pattern Structural Causal Model Causal Event Causal Knowledge Non Causal