  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.
