Constraint Based Causal Discovery

Constraint-based causal discovery aims to infer causal relationships between variables from observational data by leveraging conditional independence tests. Current research focuses on improving the efficiency and robustness of these methods, particularly by reducing the number of tests needed, developing more powerful tests for various data types (including continuous, categorical, and time series data), and incorporating domain knowledge or handling biases like selection bias and confounding. These advancements are crucial for advancing scientific understanding across diverse fields by enabling more accurate and efficient causal inference from complex datasets, leading to improved model building and decision-making.

Papers

June 11, 2024

Large Language Models for Constrained-Based Causal Discovery
Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls
Causal Graph Constraint Based Causal Discovery Data Driven Causal

June 3, 2024

Causal Discovery with Fewer Conditional Independence Tests
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Causal Discovery Causal Graph Conditional Independence Constraint Based Causal Discovery Ground Truth Causal

February 28, 2024

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus
Causal Discovery Causal Structure Conditional Independence Stochastic Process Stochastic Dynamical System Constraint Based Causal Discovery Kernel Two Sample Test

October 20, 2023

On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
Application Proficiency Causal Discovery Sample Complexity Conditional Independence Constraint Based Causal Discovery Von Mises

October 17, 2023

Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
Oana-Iuliana Popescu, Andreas Gerhardus, Jakob Runge
New Method Conditional Independence Conditional Mutual Information Constraint Based Causal Discovery

May 9, 2023

Causal Discovery via Conditional Independence Testing with Proxy Variables
Mingzhou Liu, Xinwei Sun, Yu Qiao, Yizhou Wang
Causal Discovery Conditional Independence Causal Relation Causal Knowledge Constraint Based Causal Discovery

February 7, 2023

CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Time Series Causal Discovery Causal Relationship Causal Relation Causal Discovery Method Constraint Based Causal Discovery Temporal Causal Discovery

January 22, 2023

Characterization and Learning of Causal Graphs with Small Conditioning Sets
Murat Kocaoglu
LeArning Abstract Causal Discovery Causal Graph New Characterization Causal Discovery Algorithm Constraint Based Causal Discovery

August 25, 2022

Learning Relational Causal Models with Cycles through Relational Acyclification
Ragib Ahsan, David Arbour, Elena Zheleva
Causal Discovery Constraint Based Causal Discovery Relational Causal Model

August 17, 2022

Domain Knowledge in A*-Based Causal Discovery
Steven Kleinegesse, Andrew R. Lawrence, Hana Chockler
Causal Discovery Domain Knowledge Causal Relationship Constraint Based Causal Discovery Based Causal Discovery Gradient Faithfulness

June 16, 2022

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
Causal Discovery Limited Data Causal Discovery Method Cause Effect Empirical Bayes Constraint Based Causal Discovery Frequentist Inference

March 3, 2022

Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris M. Mooij
Causal Relation Selection Bias Causal Interpretation Constraint Based Causal Discovery