Aug 27, 2020 · In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders.
In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders.
Results demonstrate that our model can generate unbiased and accurate treatment effect by conditioning both time-varying observed and hidden confounders, paving ...
Deep Sequential Weighting (DSW) is proposed for estimating ITE with time-varying confounders and can generate unbiased and accurate treatment effect by ...
Estimating Individual Treatment Effects with Time-Varying ...
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... These hidden confounders modeled by the latent variables were calculated by Recurrent Neural Network with multitask output and variational dropout (Fig. 3).
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Motivated by substantive considerations in sociological research, we propose a simplifying solution for the analysis of causal effects with time-varying ...
Mar 16, 2023 · This article clarifies how the biostatistical literature on time-varying treatments (TVT) can provide tools for dealing with time-varying confounding in ...
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Mar 25, 2024 · In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized ...
Longitudinal study designs are frequently used to investigate the effects of a naturally observed predictor (treatment) on an outcome over time.
Marginal structural models aim to appropriately control for the effects of time- dependent confounders affected by prior treatment. We describe how to fit these ...