9 citations · 9 across the 5 of their papers we have counts for
5 papers
Adaptive Proximal Causal Inference with Some Invalid Proxies
Prabrisha Rakshit, Xu Shi, Eric Tchetgen Tchetgen
Proximal causal inference (PCI) is a recently proposed framework to identify and estimate the causal effect of an exposure on an outcome in the presence of hidden confounders, usin…
Statistical Inference in High-dimensional Poisson Regression with Applications to Mediation Analysis
Prabrisha Rakshit, Zijian Guo
Large-scale datasets with count outcome variables are widely present in various applications, and the Poisson regression model is among the most popular models for handling count o…
Local Effects of Continuous Instruments without Positivity
Prabrisha Rakshit, Alexander Levis, Luke Keele
Instrumental variables are a popular study design for the estimation of treatment effects in the presence of unobserved confounders. In the canonical instrumental variables design,…
SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models
Prabrisha Rakshit, Zhenyu Wang, T. Tony Cai +1
We introduce the R package \CRANpkg{SIHR} for statistical inference in high-dimensional generalized linear models with continuous and binary outcomes. The package provides function…
Inference for the Case Probability in High-dimensional Logistic Regression
Zijian Guo, Prabrisha Rakshit, Daniel S. Herman +1
Labeling patients in electronic health records with respect to their statuses of having a disease or condition, i.e. case or control statuses, has increasingly relied on prediction…