Causal Regularization
By Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, Jimeng Sun. 2017
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.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Edward Choi
- Christina Heinze-Deml
- Jimeng Sun
- Mohammad Taha Bahadori
- Bernhard Schölkopf
- Nicolai Meinshausen
- Walter F. Stewart
- Krzysztof Chalupka
Citations in the corpus
- Causal Regularization (2019, background): “After submission I became aware of a preprint with the same title as mine, [11] where regularizers are constructed that are tailored particularly for causal features.”
- Conditional variance penalties and domain shift robustness (2017, background): “Bahadori et al. (2017) devise a regularizer that combines an (cid:96) 1 penalty with weights corresponding to the estimated probability of the respective feature being causal for the target.”