Oct 28, 2020 · We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) by incorporating recurrent neural networks into two-phase adjustments for the existence of ...
Sep 7, 2024 · How to model complex high-dimensional dependency in the data? To address these challenges, we propose Deep Recurrent Inverse TreatmEnt weighting ...
Oct 28, 2020 · DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data that can be better ...
Oct 28, 2020 · Figure 1: DeepRite: A generic pipeline of using recurrent inverse treatment weighting for adjusting time-varying con- founding in observational ...
In this article we introduce the concept of IPTW and describe in which situations this method can be applied to adjust for measured confounding in observational ...
Missing: DeepRite: Deep
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data · Yanbo XuCao XiaoJimeng ...
In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders. Specifically, DSW infers the hidden confounders by ...
Mar 18, 2022 · This study aims to explore which confounding adjustment methods have been used in longitudinal observational data to estimate a treatment effect.
Missing: DeepRite: Deep Recurrent Modern
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data · no code ...
To address these challenges, we propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) by incorporating recurrent neural networks into two-phase ...