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Causal Learning via Manifold Regularization.

ABSTRACT: 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.

SUBMITTER: Hill SM

PROVIDER: S-EPMC6986916 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Causal Learning via Manifold Regularization.

Hill Steven M SM Oates Chris J CJ Blythe Duncan A DA Mukherjee Sach S

Journal of machine learning research : JMLR 20190101

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 biv ...[more]

PMID: 31992961

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