Machine Learning – Publications
1.
Javaloy, Adrián; Sánchez-Mart'ın, Pablo; Valera, Isabel
Causal normalizing flows: from theory to practice Journal Article Oral
In: CoRR, vol. abs/2306.05415, 2023.
Abstract | Links | BibTeX | Tags: adrian, isabel, oral, pablo
@article{DBLP:journals/corr/abs-2306-05415,
title = {Causal normalizing flows: from theory to practice},
author = {Adrián Javaloy and Pablo Sánchez-Mart'ın and Isabel Valera},
url = {https://doi.org/10.48550/arXiv.2306.05415},
doi = {10.48550/arXiv.2306.05415},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {CoRR},
volume = {abs/2306.05415},
abstract = {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.},
keywords = {adrian, isabel, oral, pablo},
pubstate = {published},
tppubtype = {article}
}
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.