Causal Learning via Manifold Regularization

TL;DR

This paper introduces a novel machine learning approach for causal structure estimation by treating causal indicators as labels and using manifold regularization with biological data, including experimental verification, to improve causal inference.

Contribution

It develops a distance-based manifold regularization method for causal learning, integrating scientific knowledge and interventional data, and demonstrates its effectiveness on biological datasets.

Findings

Effective causal inference on biological data

Incorporates scientific knowledge and interventions

Simple and general approach

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.