2023/06/08 by Adrián Javaloy, Javaloy, Adrián, Pablo Sánchez-Martín +4 · 1 voice · 12 citations
Computer Science · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #Topic Modeling #cs.AI #cs.LG #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.2306.05415
openalex publication_date 2023/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically, we first leverage recent results on non-linear ICA to show that causal models are identifiable from observational data given a causal ordering, and thus can be recovered using autoregressive normalizing flows (NFs). Second, we analyze different design and learning choices for causal normalizing flows to capture the underlying causal data-generating process. Third, we describe how to implement the do-operator in causal NFs, and thus, how to answer interventional and counterfactual questions. Finally, in our experiments, we validate our design and training choices through a comprehensive ablation study; compare causal NFs to other approaches for approximating causal models; and empirically demonstrate that causal NFs can be used to address real-world problems, where the presence of mixed discrete-continuous data and partial knowledge on the causal graph is the norm. The code for this work can be found at https://github.com/psanch21/causal-flows.