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

November 18, 2024

On the physics of nested Markov models: a generalized probabilistic theory perspective
Xingjian Zhang, Yuhao Wang
Causal Structure Causal Model Theoretical Physic High Dimensional Causal

November 12, 2024

Language Models as Causal Effect Generators
Lucius E.J. Bynum, Kyunghyun Cho
Language Model Causal Inference Causal Structure Causal Model Structural Causal Model Novel Causal Model

November 11, 2024

SPARTAN: A Sparse Transformer Learning Local Causation
Anson Lei, Ingmar Posner, Bernhard Schölkopf
Causal Graph Causal Structure Causal Model Sparse Transformer Spatio Temporal Transformer

November 8, 2024

Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum, Dominik Janzing
Causal Discovery Causal Model Causal Discovery Algorithm Leave One Out Cross Validation

November 5, 2024

Graph Agnostic Causal Bayesian Optimisation
Sumantrak Mukherjee, Mengyan Zhang, Seth Flaxman, Sebastian Josef Vollmer
Causal Model Bayesian Optimisation Heterogeneous Causal Causal Bayesian Optimization

October 31, 2024

GPT or BERT: why not both?
Lucas Georges Gabriel Charpentier, David Samuel
Language Model Ticket BERT Causal Model GPT Neo Masked Language Training Time

October 28, 2024

Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots
Pablo de los Riscos, Fernando Corbacho
Latent Variable Autonomous Robot Causal Model Structural Causal Model Temporal Causal Active Causal

October 27, 2024

Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
Martin Rabel, Wiebke Günther, Jakob Runge, Andreas Gerhardus
Causal Graph Causal Structure Causal Model Context Awareness Observational Study

October 23, 2024

Longitudinal Causal Image Synthesis
Yujia Li, Han Li, ans S. Kevin Zhou
Causal Graph Causal Model Counterfactual Image Longitudinal Image Synthetic MRI

October 18, 2024

CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models
Yanming Zhang, Akshith Kota, Eric Papenhausen, Klaus Mueller
Causal Model Causal Relation Causal Knowledge Causal Network

October 16, 2024

Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting
System Description Causal Graph Causal Model Causal Relation Causal Knowledge Causal Feature

October 14, 2024

A Practical Approach to Causal Inference over Time
Martina Cinquini, Isacco Beretta, Salvatore Ruggieri, Isabel Valera
Causal Inference Time Matter Autoregressive Model Causal Model Causal Intervention Practical Approach

October 9, 2024

Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
Causal Discovery Causal Structure Causal Model Causal Representation Learning Temporal Data Latent Causal

October 8, 2024

TRACE: Temporal Grounding Video LLM via Causal Event Modeling
Yongxin Guo, Jingyu Liu, Mingda Li, Xiaoying Tang, Qingbin Liu, Xi Chen
Causal Model Video LLM Fuzzy Trace Video Temporal Grounding Video Understanding Model

October 3, 2024

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series
Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Causal Discovery Causal Structure Causal Model Causal Relationship Causal Analysis K Content Interventional Data

October 2, 2024

Induced Covariance for Causal Discovery in Linear Sparse Structures
Saeed Mohseni-Sehdeh, Walid Saad
Causal Discovery Causal Model Causal Relationship Cause Effect Sparse Structure Covariance Conditioning Causal Discovery Algorithm Sparse Causal

September 25, 2024

Counterfactual Token Generation in Large Language Models
Ivi Chatzi, Nina Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez
Large Language Model Causal Model High Quality Counterfactuals Counterfactual Generation Token Generation

September 22, 2024

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
Causal Graph Causal Model Conditional Independence Causal Inference Task

September 20, 2024

A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns
Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar, Zachary C. Lipton, Liu Leqi
Recommender System Causal Model Recommendation Algorithm Performance Metric Ethical Implication

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