Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
This work addresses the challenge of personalized medicine by providing a method to pinpoint individual disease causes, though it appears incremental as it builds on existing structural equation models.
The paper tackles the problem of identifying patient-specific root causes of complex diseases by equating them to error terms in a structural equation model, and it introduces a generalized heteroscedastic noise model with a customized algorithm called GRCI that recovers these causes more accurately than existing alternatives.
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 $Y = m(X) + \varepsilonσ(X)$ with non-linear functions $m(X)$ and $σ(X)$ 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.