In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to steer predictive models towards causally-interpretable solutions and theoretically study its properties. In a large-scale analysis of Electronic Health Records (EHR), our causally-regularized model outperforms its L1-regularized counterpart in causal accuracy and is competitive in predictive performance. We perform non-linear causality analysis by causally regularizing a special neural network architecture. We also show that the proposed causal regularizer can be used together with neural representation learning algorithms to yield up to 20% improvement over multilayer perceptron in detecting multivariate causation, a situation common in healthcare, where many causal factors should occur simultaneously to have an effect on the target variable.
@article{arxiv.1702.02604,
title = {Causal Regularization},
author = {Mohammad Taha Bahadori and Krzysztof Chalupka and Edward Choi and Robert Chen and Walter F. Stewart and Jimeng Sun},
journal= {arXiv preprint arXiv:1702.02604},
year = {2017}
}