Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

Karren Yang, Abigail Katcoff, Caroline Uhler
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5541-5550, 2018.

Abstract

We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser & Buhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-yang18a, title = {Characterizing and Learning Equivalence Classes of Causal {DAG}s under Interventions}, author = {Yang, Karren and Katcoff, Abigail and Uhler, Caroline}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {5541--5550}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/yang18a/yang18a.pdf}, url = {https://proceedings.mlr.press/v80/yang18a.html}, abstract = {We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser & Buhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.} }
Endnote
%0 Conference Paper %T Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions %A Karren Yang %A Abigail Katcoff %A Caroline Uhler %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-yang18a %I PMLR %P 5541--5550 %U https://proceedings.mlr.press/v80/yang18a.html %V 80 %X We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser & Buhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.
APA
Yang, K., Katcoff, A. & Uhler, C.. (2018). Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:5541-5550 Available from https://proceedings.mlr.press/v80/yang18a.html.

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