Causal Structure Learning

Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample

Nov 1, 2026
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

May 7, 2025
Causal Foundations for Pragmatic Data Science

Feb 7, 2025
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables

Jul 13, 2023
Causal Structure Learning: a Combinatorial Perspective

Aug 1, 2022
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors

Apr 11, 2022
DCI: Learning Causal Differences between Gene Regulatory Networks

Difference causal inference (DCI) identifies changes in gene regulatory relationships across two conditions directly from their expression data. Rather than reconstructing both complete networks, the algorithm estimates which causal edges appear, disappear, or change strength, reducing sample and computational requirements. A Python implementation includes stability selection across tuning parameters. Applications to single-cell RNA sequencing illustrate network comparison and validation through intervention-effect prediction.

Sep 1, 2021
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing

Feb 15, 2021
Active Structure Learning of Causal DAGs via Directed Clique Trees

Nov 1, 2020
Permutation-Based Causal Structure Learning with Unknown Intervention Targets

Oct 20, 2019