ABCD-strategy: Budgeted experimental design for targeted causal structure discovery
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agrawal19b.pdf
Description
Published version
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1.44 MB
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Adobe PDF
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Author(s)
Uhler, Caroline
Agrawal, Raj
Squires, Chandler
Yang, Karren
Shanmugam, Karthikeyan
Date Issued
2019
Journal
AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics
Citation
Uhler, Caroline, Agrawal, Raj, Squires, Chandler, Yang, Karren and Shanmugam, Karthikeyan. 2019. "ABCD-strategy: Budgeted experimental design for targeted causal structure discovery." AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 89.
Version
Final published version
Abstract
© 2019 by the author(s). Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited interventional data. Given that randomized experiments are usually expensive to perform, we propose a general framework and theory based on optimal Bayesian experimental design to select experiments for targeted causal discovery. That is, we assume the experimenter is interested in learning some function of the unknown graph (e.g., all descendants of a target node) subject to design constraints such as limits on the number of samples and rounds of experimentation. While it is in general computationally intractable to select an optimal experimental design strategy, we provide a tractable implementation with provable guarantees on both approximation and optimization quality based on submodularity. We evaluate the efficacy of our proposed method on both synthetic and real datasets, thereby demonstrating that our method realizes considerable performance gains over baseline strategies such as random sampling.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
MIT-IBM Watson AI Lab
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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
http://proceedings.mlr.press/v89/agrawal19b.html
