Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model

About

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.

Eric V. Strobl, Thomas A. Lasko · 2022

Related benchmarks

Task	Dataset	Result
Causal Discovery	Tübingen	AUROC—	37
Bivariate Causal Discovery	D4 s1	Accuracy67	33
Bivariate Causal Discovery	LS	Accuracy64	33
Bivariate Causal Discovery	NN-V	Accuracy60	33
Bivariate Causal Discovery	SIM-c	Accuracy65	33
Bivariate Causal Discovery	PER	Accuracy56	33
Bivariate Causal Discovery	SIM	Accuracy55	33
Bivariate Causal Discovery	AN	Accuracy67	33
Bivariate Causal Discovery	Net	Accuracy58	33
Bivariate Causal Discovery	Qd-V	Accuracy47	33