Pathway Lasso: pathway estimation and selection with high-dimensional mediators
Yi Zhao, Xi Luo
Abstract
. This penalty function is a convex relaxation of the non-convex product function for the mediation effects, and it enables a computationally tractable optimization criterion to estimate and select pathway effects simultaneously. We develop a fast ADMM-type algorithm to compute the model parameters, and we show that the iterative updates can be expressed in closed form. We also prove the asymptotic consistency of our Pathway Lasso estimator for the mediation effect. On both simulated data and an fMRI data set, the proposed approach yields higher pathway selection accuracy and lower estimation bias than competing methods.
Topics & Concepts
Lasso (programming language)EstimatorMathematical optimizationModel selectionComputer scienceFunction (biology)Selection (genetic algorithm)MediationConsistency (knowledge bases)AlgorithmMathematicsArtificial intelligenceStatisticsEvolutionary biologyLawWorld Wide WebBiologyPolitical scienceAdvanced Causal Inference TechniquesHealth Systems, Economic Evaluations, Quality of LifeStatistical Methods and Inference