In this section, we first give the notations and assumptions for treatment effect estimation in observational studies, then propose a series of propositions to ...
Feb 14, 2022 · Abstract: In observational studies, confounder separation and balancing are the fundamental problems of treatment effect estimation.
In observational studies, confounder separation and balancing are the fundamental problems of treatment effect estimation. Most of the previous methods ...
The fundamental problem in treatment effect estimation from ob- servational data is confounder identification and balancing. Most of the previous methods ...
This repository contains the implementation code for paper: Learning Decomposed Representations for Treatment Effect Estimation
Jan 15, 2024 · In this paper, we propose a novel representation balancing model, DIGNet, for treatment effect estimation. DIGNet incorporates two key components, PDIG and PPBR ...
Oct 22, 2024 · In observational studies, confounder separation and balancing are the fundamental problems of treatment effect estimation.
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Aug 6, 2024 · This article proposes a multitask learning framework called Decomposed-Representation based Causality Estimating Model (DRCEM), which identifies confounding ...
Estimating the individual treatment effect (ITE) from observational data is an important issue both theoretically and practically.
proposed a multi-task deep counterfactual network for treatment effect estimation by learning shared representations for treated and control outcomes and ...