70 citations · 71 across the 5 of their papers we have counts for
5 papers
A Riesz Representer Perspective on Targeted Learning
Salvador V. Balkus, Christian Testa, Nima S. Hejazi
As research in causal inference has sought to address more complex scientific questions, the number of specialized estimands in the field has proliferated. Recognition that many of…
Linear models for causal inference under network interference
Eric Tong, Salvador V. Balkus
In causal inference, interference occurs when the treatment of one unit may affect the outcomes of other units. The goal of this work is to serve as a guide to the use of linear ou…
On the use of cross-fitting in causal machine learning with correlated units
Salvador V. Balkus, Hasan Laith, Nima S. Hejazi
In causal machine learning, the fitting and evaluation of nuisance models are often performed on separate partitions, or folds, of the observed data. This technique, called cross-f…
The causal effects of modified treatment policies under network interference
Salvador V. Balkus, Scott W. Delaney, Nima S. Hejazi
Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal eff…
Improving Short Text Classification With Augmented Data Using GPT-3
Salvador Balkus, Donghui Yan
GPT-3 is a large-scale natural language model developed by OpenAI that can perform many different tasks, including topic classification. Although researchers claim that it requires…