Causal inference and policy optimization system based on deep learning models
US11354566B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 21, 2021 |
| Grant date | Jun 7, 2022 |
| Priority date | — |
| Expiry date | Oct 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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(1),z. The trained treatment model is executed to compute an estimated treatment variable value {circumflex over (t)}i 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 x(2) and an estimated treatment variable t. The trained outcome model is executed to compute an estimated first unknown function value {circumflex over (α)}(xi(2)) and an estimated second unknown function value {circumflex over (β)}(xi(2)) for each observation vector. An influence function value is computed for a parameter of interest using {circumflex over (α)}(xi(2)) and {circumflex over (β)}(xi(2)). A value is computed for the predefined parameter of interest using the computed influence function value.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.