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Title:On the finite sample complexity of causal discovery and the value of domain expertise
Author(s):Wadhwa, Samir
Advisor(s):Dong, Roy
Contributor(s):Dullerud, Geir Eirik
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):causality
discrete Bayesian networks
conditional independence testing
family-wise error rate
Abstract:Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the seminal works in this area is the Inferred Causation (IC) algorithm, which guarantees successful causal discovery under the assumption of a conditional independence (CI) oracle: an oracle that can states whether two random variables are conditionally independent given another set of random variables. Practical implementations of this algorithm incorporate statistical tests for conditional independence, in place of a CI oracle. In this thesis, we analyze the sample complexity of causal discovery algorithms without a CI oracle: given a certain level of confidence, how many data points are needed for a causal discovery algorithm to identify a causal structure? Furthermore, our methods allow us to quantify the value of domain expertise in terms of data samples. Finally, we demonstrate the accuracy of these sample rates with numerical examples, and quantify the benefits of three types of domain expertise: sparsity priors, known causal directions, and known conditional dependencies.
Issue Date:2021-04-28
Type:Thesis
URI:http://hdl.handle.net/2142/110577
Rights Information:Copyright 2021 Samir Wadhwa
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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