Permutation-based causal structure learning with unknown intervention targets
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squires20a.pdf
Description
Published version
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487.02 KB
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Adobe PDF
Checksum (MD5)
f3819ad9fae29bc423dfcaa5efc0cad4
Author(s)
Squires, C
Wang, Y
Uhler, C
Date Issued
January 1, 2020
Journal
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020
Citation
Squires, C, Wang, Y and Uhler, C. 2020. "Permutation-based causal structure learning with unknown intervention targets." Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, 124.
Version
Final published version
Abstract
We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene knockout technologies are known to have off-target effects. We characterize the interventional Markov equivalence class of DAGs that can be identified from interventional data with unknown intervention targets. In addition, we propose a provably consistent algorithm for learning the interventional Markov equivalence class from such data. The proposed algorithm greedily searches over the space of permutations to minimize a novel score function. The algorithm is nonparametric, which is particularly important for applications to genomics, where the relationships between variables are often non-linear and the distribution non-Gaussian. We demonstrate the performance of our algorithm on synthetic and biological datasets. Links to an implementation of our algorithm and to a reproducible code base for our experiments can be found at https://uhlerlab.github.io/causaldag/utigsp.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://proceedings.mlr.press/v124/squires20a.html
