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Experiment Selection for Causal Discovery

Hyttinen, Antti and Eberhardt, Frederick and Hoyer, Patrik O. (2013) Experiment Selection for Causal Discovery. Journal of Machine Learning Research, 14 . pp. 3041-3071. ISSN 1532-4435. https://resolver.caltech.edu/CaltechAUTHORS:20140123-114755984

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Abstract

Randomized controlled experiments are often described as the most reliable tool available to scientists for discovering causal relationships among quantities of interest. However, it is often unclear how many and which different experiments are needed to identify the full (possibly cyclic) causal structure among some given (possibly causally insufficient) set of variables. Recent results in the causal discovery literature have explored various identifiability criteria that depend on the assumptions one is able to make about the underlying causal process, but these criteria are not directly constructive for selecting the optimal set of experiments. Fortunately, many of the needed constructions already exist in the combinatorics literature, albeit under terminology which is unfamiliar to most of the causal discovery community. In this paper we translate the theoretical results and apply them to the concrete problem of experiment selection. For a variety of settings we give explicit constructions of the optimal set of experiments and adapt some of the general combinatorics results to answer questions relating to the problem of experiment selection.


Item Type:Article
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http://jmlr.org/papers/v14/hyttinen13a.htmlPublisherArticle
Additional Information:© 2013 Antti Hyttinen, Frederick Eberhardt and Patrik O. Hoyer. Submitted 7/12; Revised 4/13; Published 10/13. The authors would like to thank M. Koivisto and P. Kaski for helpful discussions and three anonymous reviewers for their helpful comments that improved the article. A.H. and P.O.H. were supported by the Academy of Finland. F.E. was supported by a grant from the James S. McDonnell Foundation on ‘Experimental Planning and the Unification of Causal Knowledge’.
Funders:
Funding AgencyGrant Number
James S. McDonnell FoundationUNSPECIFIED
Academy of FinlandUNSPECIFIED
Subject Keywords:causality, randomized experiments, experiment selection, separating systems, completely separating systems, cut-coverings
Record Number:CaltechAUTHORS:20140123-114755984
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140123-114755984
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:43493
Collection:CaltechAUTHORS
Deposited By: John Wade
Deposited On:23 Jan 2014 21:34
Last Modified:03 Oct 2019 06:08

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