Abhishek Chakrabortty | DeepAI

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∙ 05/22/2023

Semi-Supervised Causal Inference: Generalizable and Double Robust Inference for Average Treatment Effects under Selection Bias with Decaying Overlap

Average treatment effect (ATE) estimation is an essential problem in the...

0 Yuqian Zhang, et al. ∙

∙ 01/25/2022

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

We consider quantile estimation in a semi-supervised setting, characteri...

1 Abhishek Chakrabortty, et al. ∙

∙ 01/03/2022

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

In this article, we aim to provide a general and complete understanding ...

0 Abhishek Chakrabortty, et al. ∙

∙ 04/14/2021

Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap

Semi-supervised (SS) inference has received much attention in recent yea...

0 Yuqian Zhang, et al. ∙

∙ 11/26/2019

High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework

We consider high dimensional M-estimation in settings where the response...

17 Abhishek Chakrabortty, et al. ∙

∙ 09/27/2018

Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models

We consider the problem of identifying intermediate variables (or mediat...

0 Abhishek Chakrabortty, et al. ∙

∙ 04/08/2018

Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression

Concentration inequalities form an essential toolkit in the study of hig...

0 Arun Kumar Kuchibhotla, et al. ∙

∙ 01/18/2017

Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes

We consider the recovery of regression coefficients, denoted by β_0, for...

0 Abhishek Chakrabortty, et al. ∙

∙ 01/17/2017

Efficient and Adaptive Linear Regression in Semi-Supervised Settings

We consider the linear regression problem under semi-supervised settings...

0 Abhishek Chakrabortty, et al. ∙

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