An algorithm for direct causal learning of influences on patient outcomes.

ABSTRACT:

Objective

This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms.

Method

The DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14,400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-sc

SUBMITTER: Rathnam C 

PROVIDER: S-EPMC5415921 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature