Sas Institute Inc.

Michael Thomas Lamm

Douglas Allan Cairns

Xilong Chen

David Bruce Elsheimer

Jan Chvosta

Ming-Chun Chang

Gunce Eryuruk Walton

Yang Zhao

A treatment model that is a first neural network is trained to optimize a treatment loss function based on a treatment variable t using a plurality of observation vectors by regressing t on x,z. The trained treatment model is executed to compute an estimated treatment variable value {circumflex over (t)}for each observation vector. An outcome model that is a second neural network is trained to optimize an outcome loss function by regressing y on xand an estimated treatment variable t. The trained outcome model is executed to compute an estimated first unknown function value {circumflex over (&#x3b1;)}(x) and an estimated second unknown function value {circumflex over (&#x3b2;)}(x) for each observation vector. An influence function value is computed for a parameter of interest using {circumflex over (&#x3b1;)}(x) and {circumflex over (&#x3b2;)}(x). A value is computed for the predefined parameter of interest using the computed influence function value.

Causal inference and policy optimization system based on deep learning models

Growing community of inventors

Cary, NC, United States of America

Michael Thomas Lamm