• Corpus ID: 245650220

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

@inproceedings{Chakrabortty2022AGF,
  title={A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings},
  author={Abhishek Chakrabortty and Guorong Dai and Eric Tchetgen Tchetgen},
  year={2022}
}
In this article, we aim to provide a general and complete understanding of semi-supervised (SS) causal inference for treatment effects. 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…