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Budgeted experiment design for causal structure learning

Ghassami, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. Publisher: International Machine Learning Society (IMLS) , 2018
  3. Abstract:
  4. We study the problem of causal structure learning when the experimenter is limited to perform at most k non-adaptive experiments of size 1. We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolved. We prove that the corresponding objective function is submodular and a greedy algorithm suffices to achieve (1 - approximation of the optimal value. We further present an accelerated variant of the greedy algorithm, which can lead to orders of magnitude performance speedup. We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions. © 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved
  5. Keywords:
  6. Artificial intelligence ; Budget control ; Learning systems ; Average numbers ; Causal structure learning ; Experiment design ; Greedy algorithms ; Objective functions ; Optimal values ; Optimization problems ; Orders of magnitude ; Approximation algorithms
  7. Source: 35th International Conference on Machine Learning, ICML 2018, 10 July 2018 through 15 July 2018 ; Volume 4 , 2018 , Pages 2788-2801 ; 9781510867963 (ISBN)