Causal Regularization

Abstract

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.

Conditional Variance Penalties and Domain Shift Robustness

“…Recently, various approaches have been proposed that leverage causal motivations for deep learning or use deep learning for causal inference, related to e.g. the problems of causeeffect inference and generative adversarial networks (Chalupka et al, 2014;Lopez-Paz and Oquab, 2017;Goudet et al, 2017;Bahadori et al, 2017;Besserve et al, 2018;Kocaoglu et al, 2018). Kilbertus et al (2017) exploit causal reasoning to characterize fairness considerations in machine learning.…”