Causal Discovery

Causal discovery aims to infer cause-and-effect relationships from data, moving beyond simple correlations to understand underlying mechanisms. Current research emphasizes developing algorithms and models, including constraint-based methods, score-matching techniques, and those leveraging neural networks (like graph neural networks and normalizing flows), to efficiently handle high-dimensional data, time series, and latent variables, often incorporating expert knowledge or interventional data to improve accuracy. This field is crucial for advancing scientific understanding across diverse domains, from biology and healthcare to climate science and robotics, by enabling more accurate modeling, prediction, and intervention design. Improved causal discovery methods are leading to more reliable insights and more effective decision-making in complex systems.

Papers

October 25, 2023

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
Causal Inference Causal Discovery Latent Confounding Exposure Data Local Causal Discovery

October 24, 2023

Causal Representation Learning Made Identifiable by Grouping of Observational Variables
Hiroshi Morioka, Aapo Hyvärinen
Causal Discovery Causal Model Group Setting Causal Representation Learning Observational Data Latent Causal Temporal Causal

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 20, 2023

October 17, 2023

October 10, 2023

Discovering Mixtures of Structural Causal Models from Time Series Data
Sumanth Varambally, Yi-An Ma, Rose Yu
Causal Discovery Time Series Data Structural Causal Model Input Mixture Causal Discovery Task

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

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 21, 2023

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Tiago da Silva, Eliezer Silva, Adèle Ribeiro, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita
Causal Inference Causal Discovery Causal Relation Energy Based GFlowNets Latent Confounding Causal Attribution Ancestral Graph

September 18, 2023

September 11, 2023

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, Kui Ren
Causal Discovery Causal Relationship Conditional Independence Extended Version Causal Discovery Algorithm Runtime Verification

August 23, 2023

Benchmarking Causal Study to Interpret Large Language Models for Source Code
Daniel Rodriguez-Cardenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk
Code Generation Causal Inference Causal Discovery Source Code Code Summarization

Causal Discovery

Papers

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Causal Representation Learning Made Identifiable by Grouping of Observational Variables

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Efficient Causal Discovery for Robotics Applications

Shortcuts for causal discovery of nonlinear models by score matching

Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery

On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery

Assumption violations in causal discovery and the robustness of score matching

Causal discovery using dynamically requested knowledge

Causal Feature Selection via Transfer Entropy

Discovering Mixtures of Structural Causal Models from Time Series Data

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

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

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems

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

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets

Causal Discovery and Counterfactual Explanations for Personalized Student Learning

Causal Discovery and Prediction: Methods and Algorithms

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)

Benchmarking Causal Study to Interpret Large Language Models for Source Code