Causal Regularization

  title={Causal Regularization},
  author={Mohammad Taha Bahadori and Krzysztof Chalupka and Edward Choi and Robert Chen and Walter F. Stewart and Jimeng Sun},
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… CONTINUE READING
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