Neural Causal Regularization under the Independence of Mechanisms Assumption
Neural networks provide a powerful framework for learning the association between input and response variables and making accurate predictions. However, in many applications such as healthcare, it is important to identify causal relationships between the inputs and the response variables to be able to change the response variables by intervention on the inputs. In pursuit of models whose predictive power comes maximally from causal variables, we propose a novel causal regularizer based on the independence of mechanisms assumption. We utilize the causal regularizer to steer deep neural network architectures towards causally-interpretable solutions. We perform a large-scale analysis of electronic health records. Employing expert*s judgment as the causal ground-truth, we show that our causally-regularized algorithm outperforms its L1-regularized equivalence both in predictive performance as well as causal relevance. Finally, we show that the proposed causal regularizer can be used together with representation learning algorithms to yield up to 20% improvement in the causality score of the generated hypotheses.
This paper proposes to use a causality score to weight a sparsity regularizer. In that way, selected variables trade off between being causal and discriminative. The framework is primarily evaluated on a proprietary health dataset. While the dataset does give a good motivation to the problem setting, the paper falls a bit short for ICLR due to the lack of additional controlled experiments, relatively straightforward methodology (given the approach of Chalupka et al., arXiv Preprint, 2016, which is a more interesting paper from a technical perspective), and paucity of theoretical motivation. At the core of this paper, the approach is effectively to weight a sparsity regularizer so that *causal* variables (as determined by a separate objective) are more likely to be selected. This is generally a good idea, but we do not get a proper validation of this from the experiments as ground truth is absent. A theorem on identifiability of causal+discriminative variables from a data sample combined with adequate synthetic experiments would have probably been sufficient, for example, to push the paper towards accept from a technical perspective, but as it is, it is lacking in insight and reproducibility.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Causal Regularization under the Independence of Mechanisms Assumption
Neural networks provide a powerful framework for learning the association between input and response variables and making accurate predictions. However, in many applications such as healthcare, it is important to identify causal relationships between the inputs and the response variables to be able to change the response variables by intervention on the inputs. In pursuit of models whose predictive power comes maximally from causal variables, we propose a novel causal regularizer based on the independence of mechanisms assumption. We utilize the causal regularizer to steer deep neural network architectures towards causally-interpretable solutions. We perform a large-scale analysis of electronic health records. Employing expert*s judgment as the causal ground-truth, we show that our causally-regularized algorithm outperforms its L1-regularized equivalence both in predictive performance as well as causal relevance. Finally, we show that the proposed causal regularizer can be used together with representation learning algorithms to yield up to 20% improvement in the causality score of the generated hypotheses.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes to use a causality score to weight a sparsity regularizer. In that way, selected variables trade off between being causal and discriminative. The framework is primarily evaluated on a proprietary health dataset. While the dataset does give a good motivation to the problem setting, the paper falls a bit short for ICLR due to the lack of additional controlled experiments, relatively straightforward methodology (given the approach of Chalupka et al., arXiv Preprint, 2016, which is a more interesting paper from a technical perspective), and paucity of theoretical motivation. At the core of this paper, the approach is effectively to weight a sparsity regularizer so that *causal* variables (as determined by a separate objective) are more likely to be selected. This is generally a good idea, but we do not get a proper validation of this from the experiments as ground truth is absent. A theorem on identifiability of causal+discriminative variables from a data sample combined with adequate synthetic experiments would have probably been sufficient, for example, to push the paper towards accept from a technical perspective, but as it is, it is lacking in insight and reproducibility.
assistant
This paper proposes to use a causality score to weight a sparsity regularizer. In that way, selected variables trade off between being causal and discriminative. The framework is primarily evaluated on a proprietary health dataset. While the dataset does give a good motivation to the problem setting, the paper falls a bit short for ICLR due to the lack of additional controlled experiments, relatively straightforward methodology (given the approach of Chalupka et al., arXiv Preprint, 2016, which is a more interesting paper from a technical perspective), and paucity of theoretical motivation. At the core of this paper, the approach is effectively to weight a sparsity regularizer so that *causal* variables (as determined by a separate objective) are more likely to be selected. This is generally a good idea, but we do not get a proper validation of this from the experiments as ground truth is absent. A theorem on identifiability of causal+discriminative variables from a data sample combined with adequate synthetic experiments would have probably been sufficient, for example, to push the paper towards accept from a technical perspective, but as it is, it is lacking in insight and reproducibility.