A neural-net-transformed disease label is fed into causal discovery, and the resulting 'causal strength' ranks are compared with ML feature importance on heart failure EHR data, with the comparison likely inflated by the proxy's circularity.
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
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abstract
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering of variables in a linear acyclic model without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model.
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Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
A neural-net-transformed disease label is fed into causal discovery, and the resulting 'causal strength' ranks are compared with ML feature importance on heart failure EHR data, with the comparison likely inflated by the proxy's circularity.