Causal Graph Discovery

Causal graph discovery aims to infer the causal relationships between variables from observational or interventional data, ultimately representing these relationships as a directed acyclic graph. Current research focuses on improving the efficiency and accuracy of discovery algorithms, including constraint-based and score-based methods, often incorporating techniques like differential privacy to protect sensitive data and leveraging large language models to integrate prior knowledge and improve the directionality of inferred edges. These advancements are significant for various fields, enabling more robust causal inference in areas such as healthcare, social sciences, and engineering, leading to better decision-making and improved understanding of complex systems.

Papers

September 27, 2024

CURATE: Scaling-up Differentially Private Causal Graph Discovery
Payel Bhattacharjee, Ravi Tandon
Differential Privacy Causal Graph Privacy Budget Automatic Curation Causal Graph Discovery

May 22, 2024

Large Language Models are Effective Priors for Causal Graph Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Causal Graph Causal Graph Discovery Causal Structure Discovery

February 23, 2024

Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
Yuzhe Zhang, Yipeng Zhang, Yidong Gan, Lina Yao, Chen Wang
Retrieval Augmented Generation Causal Graph Causal Direction Causal Graph Discovery

February 13, 2024

Causal Discovery under Off-Target Interventions
Davin Choo, Kirankumar Shiragur, Caroline Uhler
Causal Discovery Causal Graph Causal Graph Discovery

February 2, 2024

Efficient Causal Graph Discovery Using Large Language Models
Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, Yoshua Bengio
Causal Graph Causal Relationship Causal Graph Discovery

June 11, 2023

Differentially Private Conditional Independence Testing
Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
Differential Privacy Conditional Independence Private Hypothesis Causal Graph Discovery Conditional Randomization

June 9, 2023

Adaptivity Complexity for Causal Graph Discovery
Davin Choo, Kirankumar Shiragur
Causal Discovery Causal Graph Adaptive Algorithm Adaptive Model Ground Truth Causal Causal Graph Discovery

January 26, 2023

Causal Graph Discovery from Self and Mutually Exciting Time Series
Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran
Structural Causal Model Directed Acyclic Graph Self Information Causal Graph Discovery Link Function

August 13, 2022

Learning Linear Non-Gaussian Polytree Models
Daniele Tramontano, Anthea Monod, Mathias Drton
Graph Drawing Linear Non Gaussian Acyclic Model Causal Graph Discovery