Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model

• URL: http://arxiv.org/abs/2205.13085v2
• Date: Thu, 6 Jul 2023 20:22:20 GMT
• Title: Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
• Authors: Eric V. Strobl, Thomas A. Lasko
• Abstract summary: We focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the error terms in a structural equation model. A customized algorithm called Generalized Root Causal Inference (GRCI) is used to extract the error terms correctly.
• Score: 10.885111578191564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the exogenous error terms in a structural equation model. We generalize from the linear setting to the heteroscedastic noise model where $Y = m(X) + \varepsilon\sigma(X)$ with non-linear functions $m(X)$ and $\sigma(X)$ representing the conditional mean and mean absolute deviation, respectively. This model preserves identifiability but introduces non-trivial challenges that require a customized algorithm called Generalized Root Causal Inference (GRCI) to extract the error terms correctly. GRCI recovers patient-specific root causes more accurately than existing alternatives.

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