Risk estimation using probability machines.

ABSTRACT:

Background

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios.

Results

We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model

SUBMITTER: Dasgupta A 

PROVIDER: S-EPMC4015350 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature