Causal Regularization

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...

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Bibliographic Details
Published in:arXiv.org 2017-02
Main Authors: Mohammad Taha Bahadori, Chalupka, Krzysztof, Choi, Edward, Chen, Robert, Stewart, Walter F, Sun, Jimeng
Format: Article
Language:English
Subjects:
Algorithms
Causation
Domains
Electronic health records
Health care
Machine learning
Mathematical models
Multilayer perceptrons
Neural networks
Nonlinear analysis
Performance prediction
Regularization
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Summary: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.
ISSN:2331-8422