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Causal learning via manifold regularization

Lookup NU author(s): Professor Chris Oates

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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


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