Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings

no code implementations • 25 Jan 2022 • Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll

We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets, to improve the estimation accuracy compared to the supervised estimator, i. e., the sample quantile from the labeled data.

Dimensionality Reduction Imputation

A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings

no code implementations • 3 Jan 2022 • Abhishek Chakrabortty, Guorong Dai, Eric Tchetgen Tchetgen

Specifically, we consider two such estimands: (a) the average treatment effect and (b) the quantile treatment effect, as prototype cases, in an SS setting, characterized by two available data sets: (i) a labeled data set of size $n$, providing observations for a response and a set of high dimensional covariates, as well as a binary treatment indicator; and (ii) an unlabeled data set of size $N$, much larger than $n$, but without the response observed.

Causal Inference