3 citations · 4 across the 6 of their papers we have counts for
6 papers
Extending Prediction-Powered Inference through Conformal Prediction
Daniel Csillag, Pedro Dall'Antonia, Claudio José Struchiner +1
Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, man…
Prediction-Powered E-Values
Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert
Quality statistical inference requires a sufficient amount of data, which can be missing or hard to obtain. To this end, prediction-powered inference has risen as a promising metho…
Strategic Conformal Prediction
Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert
When a machine learning model is deployed, its predictions can alter its environment, as better informed agents strategize to suit their own interests. With such alterations in min…
Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert
Many algorithms have been recently proposed for causal machine learning. Yet, there is little to no theory on their quality, especially considering finite samples. In this work, we…
Quantifying protocols for safe school activities
Juliano Genari, Guilherme Tegoni Goedert, Sergio H. A. Lira +12
By the peak of COVID-19 restrictions on April 8, 2020, up to 1.5 billion students across 188 countries were by the suspension of physical attendance in schools. Schools were among…
Estimating the Size of Aedes aegypti Populations from Dengue Incidence Data: Implications for the Risk of Yellow Fever, Zika Virus and Chikungunya Outbreaks
Eduardo Massad, Marcos Amaku, Francisco Antonio Bezerra Coutinho +4
In this paper we present a model to estimate the density of aedes mosquitoes in a community affected by dengue. The model is based on the fitting of a continuous function to the in…