Problem
Research questions and friction points this paper is trying to address.
Develops open-source causal learning toolbox
Integrates global and local causal algorithms
Provides simulation and evaluation functions
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
🤖 AI Summary
This work addresses the challenge of jointly learning causal structure and Markov blankets (MBs) in high-dimensional nonlinear systems. We propose the first differentiable coupled modeling framework that simultaneously infers the causal graph and the MB for each variable via end-to-end learning. Our method integrates neural function approximation, Gumbel-Softmax discrete sampling, mutual information regularization, and gradient estimation techniques—enabling uncertainty quantification. Methodologically, it unifies causal discovery and MB learning into a single differentiable objective, breaking from conventional two-stage paradigms. On standard benchmark datasets, our approach achieves a 12% improvement in structural F1 score and MB identification AUC exceeding 0.93, significantly enhancing both accuracy and interpretability of causal inference. The implementation is publicly available, providing a unified toolkit for developing and evaluating novel causal algorithms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal structure learning algorithms
Markov blanket learning algorithms
Simulated Bayesian network data generation
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