UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
Journal of Educational Psychology , volume=
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A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
Data equity, prediction equity, and decision equity are distinct statistical requirements that need separate evaluations to address how racial biases in pulse oximetry measurements lead to treatment disparities.
citing papers explorer
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UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data
UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
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A Riesz Representer Perspective on Targeted Learning
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
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Data (in)equities in data science: Dissecting systemic and systematic biases in pulse oximetry
Data equity, prediction equity, and decision equity are distinct statistical requirements that need separate evaluations to address how racial biases in pulse oximetry measurements lead to treatment disparities.