779 research outputs found
Counterfactual Formulation of Patient-Specific Root Causes of Disease
Root causes of disease intuitively correspond to root vertices that increase
the likelihood of a diagnosis. This description of a root cause nevertheless
lacks the rigorous mathematical formulation needed for the development of
computer algorithms designed to automatically detect root causes from data.
Prior work defined patient-specific root causes of disease using an
interventionalist account that only climbs to the second rung of Pearl's Ladder
of Causation. In this theoretical piece, we climb to the third rung by
proposing a counterfactual definition matching clinical intuition based on
fixed factual data alone. We then show how to assign a root causal contribution
score to each variable using Shapley values from explainable artificial
intelligence. The proposed counterfactual formulation of patient-specific root
causes of disease accounts for noisy labels, adapts to disease prevalence and
admits fast computation without the need for counterfactual simulation
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
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 with
non-linear functions and 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
Sample-Specific Root Causal Inference with Latent Variables
Root causal analysis seeks to identify the set of initial perturbations that
induce an unwanted outcome. In prior work, we defined sample-specific root
causes of disease using exogenous error terms that predict a diagnosis in a
structural equation model. We rigorously quantified predictivity using Shapley
values. However, the associated algorithms for inferring root causes assume no
latent confounding. We relax this assumption by permitting confounding among
the predictors. We then introduce a corresponding procedure called Extract
Errors with Latents (EEL) for recovering the error terms up to contamination by
vertices on certain paths under the linear non-Gaussian acyclic model. EEL also
identifies the smallest sets of dependent errors for fast computation of the
Shapley values. The algorithm bypasses the hard problem of estimating the
underlying causal graph in both cases. Experiments highlight the superior
accuracy and robustness of EEL relative to its predecessors
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