The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 25, 2023

Filed:

Feb. 28, 2022
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Mengting Wan, Bellevue, WA (US);

Jing Ma, Charlottesville, VA (US);

Longqi Yang, Issaquah, WA (US);

Brent Jaron Hecht, Redmond, WA (US);

Jaime Teevan, Bellevue, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2023.01); G06Q 30/0201 (2023.01); G06N 5/04 (2023.01); G06Q 30/0601 (2023.01); G06N 3/0464 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0201 (2013.01); G06N 3/0464 (2023.01); G06N 5/04 (2013.01); G06Q 30/0631 (2013.01);
Abstract

A computing system, computer-readable storage medium, and method for individual treatment effect (ITE) estimation under high-order interference in hypergraphs are described herein. The method includes accessing, via a processor, a hypergraph dataset including multi-way interactions among nodes within each hyperedge of a corresponding hypergraph, where the hypergraph dataset corresponds to a treatment assignment for each node. The method includes performing representation learning on the hypergraph dataset to control for confounders corresponding to features of each node and to learn a confounder representation for each node. The method also includes modeling a high-order interference representation for each node by propagating the learned confounder representation and the treatment assignment for each node through a hypergraph neural network. The method further includes estimating the ITE for each node under the treatment assignment based on the learned confounder representation and the modeled high-order interference representation for each node.


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