5 papers
Bayesian Semi-supervised Inference via a Debiased Modeling Approach
Gözde Sert, Abhishek Chakrabortty, Anirban Bhattacharya
Inference in semi-supervised (SS) settings has gained substantial attention in recent years due to increased relevance in modern big-data problems. In a typical SS setting, there i…
Tail Bounds for Canonical -Statistics and -Processes with Unbounded Kernels
Abhishek Chakrabortty, Arun K. Kuchibhotla
In this paper, we prove exponential tail bounds for canonical (or degenerate) -statistics and -processes under exponential-type tail assumptions on the kernels. Most of the e…
The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
In modern large-scale observational studies, data collection constraints often result in partially labeled datasets, posing challenges for reliable causal inference, especially due…
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings
Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll
We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a…
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
Abhishek Chakrabortty, Guorong Dai
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…