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cs.LG2024★ 1 cited
Scalable Variational Causal Discovery Unconstrained by Acyclicity
Nu Hoang, Bao Duong, Thin Nguyen
Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represen…
cs.LG2024
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Nu Hoang, Bao Duong, Thin Nguyen
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility con…
cs.LG2024
Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Bao Duong, Hung Le, Biwei Huang +1
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAG…