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Semiparametric proximal causal inference
Skepticism about the assumption of no unmeasured confounding, also known...
11/17/2020 ∙ by Yifan Cui, et al. ∙
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On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Unmeasured confounding is a threat to causal inference and individualize...
10/07/2020 ∙ by Yifan Cui, et al. ∙
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An Introduction to Proximal Causal Learning
A standard assumption for causal inference from observational data is th...
09/23/2020 ∙ by Eric J. Tchetgen Tchetgen, et al. ∙
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A fiducial approach to nonparametric deconvolution problem: discrete case
Fiducial inference, as generalized by Hannig 2016 et al, is applied to n...
06/09/2020 ∙ by Yifan Cui, et al. ∙
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A Simple Weighted Approach for Instrumental Variable Estimation of Marginal Structural Mean Models
Robins 1997 introduced marginal structural models (MSMs), a general clas...
04/24/2020 ∙ by Haben Michael, et al. ∙
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Estimating heterogeneous treatment effects with right-censored data via causal survival forests
There is fast-growing literature on estimating heterogeneous treatment e...
01/27/2020 ∙ by Yifan Cui, et al. ∙
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A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fast-growing literature on estimating optimal treatment regim...
11/21/2019 ∙ by Yifan Cui, et al. ∙
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Bias-aware model selection for machine learning of doubly robust functionals
While model selection is a well-studied topic in parametric and nonparam...
11/05/2019 ∙ by Yifan Cui, et al. ∙
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Marginal Structural Models for Time-varying Endogenous Treatments: A Time-Varying Instrumental Variable Approach
Robins (1998) introduced marginal structural models (MSMs), a general cl...
09/14/2018 ∙ by Eric J. Tchetgen Tchetgen, et al. ∙
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