Latest

• Active Learning for Optimal Intervention Design in Causal Models
• Causal Imputation via Synthetic Interventions
• Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample
• Matching a Desired Causal State via Shift Interventions
• The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
• Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing
• Efficient Permutation Discovery in Causal DAGs
• Active Structure Learning of Causal DAGs via Directed Clique Trees
• Ordering-Based Causal Structure Learning in the Presence of Latent Variables
• Permutation-Based Causal Structure Learning with Unknown Intervention Targets
• Size of Interventional Markov Equivalence Classes in Random DAG Models
• ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
• Direct Estimation of Differences in Causal Graphs