86 citations · 203 across the 12 of their papers we have counts for
12 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…
Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap
Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
Semi-supervised (SS) inference has received much attention in recent years. Apart from a moderate-sized labeled data, L, the SS setting is characterized by an additional, much larg…
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
Abhishek Chakrabortty, Jiarui Lu, T. Tony Cai +1
We consider high dimensional -estimation in settings where the response is possibly missing at random and the covariates can be high dimensiona…
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
Abhishek Chakrabortty, Preetam Nandy, Hongzhe Li
We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant resear…
Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression
Arun Kumar Kuchibhotla, Abhishek Chakrabortty
Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based o…
Estimating Average Treatment Effects with a Double-Index Propensity Score
David Cheng, Abhishek Chakrabortty, Ashwin N. Ananthakrishnan +1
We consider estimating average treatment effects (ATE) of a binary treatment in observational data when data-driven variable selection is needed to select relevant covariates from…
Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes
Abhishek Chakrabortty, Matey Neykov, Raymond Carroll +1
We consider the recovery of regression coefficients, denoted by , for a single index model (SIM) relating a binary outcome to a set of possibly high dimensional…
Efficient and Adaptive Linear Regression in Semi-Supervised Settings
Abhishek Chakrabortty, Tianxi Cai
We consider the linear regression problem under semi-supervised settings wherein the available data typically consists of: (i) a small or moderate sized 'labeled' data, and (ii) a…