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Title: IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data
Networked observational data presents new opportunities for learning individual causal effects, which plays an indispensable role in decision making. Such data poses the challenge of confounding bias. Previous work presents two desiderata to handle confounding bias. On the treatment group level, we aim to balance the distributions of confounder representations. On the individual level, it is desirable to capture patterns of hidden confounders that predict treatment assignments. Existing methods show the potential of utilizing network information to handle confounding bias, but they only try to satisfy one of the two desiderata. This is because the two desiderata seem to contradict each other. When the two distributions of confounder representations are highly overlapped, then we confront the undiscriminating problem between the treated and the controlled. In this work, we formulate the two desiderata as a minimax game. We propose IGNITE that learns representations of confounders from networked observational data, which is trained by a minimax game to achieve the two desiderata. Experiments verify the efficacy of IGNITE on two datasets under various settings.  more » « less
Award ID(s):
1633381
PAR ID:
10482631
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
International Joint Conferences on Artificial Intelligence Organization
Date Published:
Journal Name:
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
ISBN:
978-0-9992411-6-5
Page Range / eLocation ID:
4534 to 4540
Format(s):
Medium: X
Location:
Yokohama, Japan
Sponsoring Org:
National Science Foundation
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