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Lookup NU author(s): Professor Chris Oates
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2019 Steven Hill, Chris Oates, Duncan Blythe, Sach Mukherjee.This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as 'labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some causal relationships and the remainder are treated as unlabelled. To illustrate the key ideas, we develop a distance-based approach (based on bivariate histograms) within a manifold regularization framework. We present empirical results on three different biological data sets (including examples where causal effects can be verified by experimental intervention), that together demonstrate the efficacy and general nature of the approach as well as its simplicity from a user's point of view.
Author(s): Hill SM, Oates CJ, Blythe DA, Mukherjee S
Publication type: Article
Publication status: Published
Journal: Journal of Machine Learning Research
Year: 2019
Volume: 20
Online publication date: 31/08/2019
Acceptance date: 30/06/2019
Date deposited: 07/10/2019
ISSN (print): 1532-4435
ISSN (electronic): 1533-7928
Publisher: Microtome Publishing
URL: http://www.jmlr.org/papers/v20/18-383.html
