Causal Structure

Causal structure research aims to uncover cause-and-effect relationships within complex systems, using both observational and interventional data to build accurate causal models. Current research focuses on developing robust algorithms for causal discovery, including those leveraging graph neural networks, score-matching techniques, and reinforcement learning to learn directed acyclic graphs (DAGs) efficiently, even in the presence of latent variables and non-linear relationships. These advancements are crucial for improving the reliability and interpretability of machine learning models across diverse fields, from healthcare and robotics to climate science and marketing, enabling more effective decision-making based on a deeper understanding of causality.

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 9, 2024

CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Jawad Chowdhury, Gabriel Terejanu
LeArning Abstract Strong Generalization Strong Consistency Causal Structure Distribution Data Causal Direction Invariant Predictor Gradient Consistency

November 5, 2024

Dissecting the Failure of Invariant Learning on Graphs
Qixun Wang, Yifei Wang, Yisen Wang, Xianghua Ying
Graph Drawing Causal Structure Node Representation Invariant Learning Invariant Risk Minimization Software Failure Ego Graph

October 30, 2024

Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
Vincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren, Stephen Zhang, Elina Robeva, Geoffrey Schiebinger
Diffusion Explainer Causal Structure Temporal Information Stochastic Differential Equation Additive Noise Real Drift Single Snapshot SDE Solver

October 28, 2024

Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics
Isabelle Lee, Joshua Lum, Ziyi Liu, Dani Yogatama
Complex Reasoning Causal Structure Semantics Surfaced Causal Direction Natural Language Reasoning Causal Intervention GPT 2

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 18, 2024

October 15, 2024

LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach
Sujai Hiremath, Promit Ghosal, Kyra Gan
Causal Discovery Causal Structure Local Search Text Based Confounders Structural Equation Additive Noise Automatic Discovery

October 13, 2024

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

Zero-Shot Learning of Causal Models
Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon
Generative Model Causal Structure Structural Causal Model Generative Process Causal Generative

October 3, 2024

October 1, 2024

September 2, 2024

A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions
Wouter A.C. van Amsterdam
Causal Structure Model Performance Disease Prediction Prediction Model Prognosis Prediction Diagnosis Prediction Case Mix

Causal Structure

Papers

On the physics of nested Markov models: a generalized probabilistic theory perspective

Language Models as Causal Effect Generators

SPARTAN: A Sparse Transformer Learning Local Causation

CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization

Dissecting the Failure of Invariant Learning on Graphs

Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots

Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics

Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support

CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers and Fully-Connected Neural Networks for Causally Constrained Predictions

Learning to refine domain knowledge for biological network inference

LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach

DAG-aware Transformer for Causal Effect Estimation

Online Multi-modal Root Cause Analysis

Causal Representation Learning in Temporal Data via Single-Parent Decoding

Zero-Shot Learning of Causal Models

Towards Understanding the Universality of Transformers for Next-Token Prediction

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series

Inferring Kernel $ε$-Machines: Discovering Structure in Complex Systems

Optimal Causal Representations and the Causal Information Bottleneck

A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions