86 citations · 203 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…