Contextualized Machine Learning
This addresses the problem of modeling heterogeneous effects in data for researchers and practitioners, but it appears incremental as it reviews and unifies existing concepts.
The paper introduces Contextualized Machine Learning, a paradigm for learning heterogeneous and context-dependent effects by using deep learning to map contextual information to context-specific parametric models, unifying frameworks like cluster analysis and cohort modeling, and provides an open-source PyTorch package.
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applying deep learning to the meta-relationship between contextual information and context-specific parametric models. This is a form of varying-coefficient modeling that unifies existing frameworks including cluster analysis and cohort modeling by introducing two reusable concepts: a context encoder which translates sample context into model parameters, and sample-specific model which operates on sample predictors. We review the process of developing contextualized models, nonparametric inference from contextualized models, and identifiability conditions of contextualized models. Finally, we present the open-source PyTorch package ContextualizedML.