
Dimension Reduction Forests: Local Variable Importance using Structured Random Forests
Random forests are one of the most popular machine learning methods due ...
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Semiparametric proximal causal inference
Skepticism about the assumption of no unmeasured confounding, also known...
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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...
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An Introduction to Proximal Causal Learning
A standard assumption for causal inference from observational data is th...
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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...
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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...
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Estimating heterogeneous treatment effects with rightcensored data via causal survival forests
There is fastgrowing literature on estimating heterogeneous treatment e...
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A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fastgrowing literature on estimating optimal treatment regim...
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Biasaware model selection for machine learning of doubly robust functionals
While model selection is a wellstudied topic in parametric and nonparam...
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Marginal Structural Models for Timevarying Endogenous Treatments: A TimeVarying Instrumental Variable Approach
Robins (1998) introduced marginal structural models (MSMs), a general cl...
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Yifan Cui
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