Causal Regularization
This addresses the need for interpretable models in domains like healthcare, where understanding causal relationships is crucial, though it is incremental by building on existing regularization and neural network methods.
The paper tackles the problem of creating accurate predictive models that are also causally interpretable, particularly in healthcare, by proposing a causal regularizer; it shows that this approach outperforms L1-regularized models in causal accuracy and achieves up to 20% improvement in detecting multivariate causation.
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.