Assistant Professor, Johns Hopkins University
2 papers at NeurIPS 2025
We introduce a graphical representation to characterize I-Markov Equivalence Class and propose a learning algorithm to integrate multiple datasets from hard interventions.
We propose a percolation-based differentiable d-separation framework that bridges constraint-based causal discovery methods with differentiable DAG structure learning.
