Abstract Machine learning (ML) has become increasingly popular in almost all scientific
disciplines, including human genetics. Owing to challenges related to sample collection and …

Inspired by the concept of active learning, we propose active inference $\unicode {x2013} $
a methodology for statistical inference with machine-learning-assisted data collection …

In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs)
are a common tool to monitor trends in causes of death (COD). VAs are interviews with a …

Prediction-based (PB) inference is increasingly used in applications where the outcome of
interest is difficult to obtain, but its predictors are readily available. Unlike traditional …

We establish a formal connection between the decades-old surrogate outcome model in
biostatistics and economics and the emerging field of prediction-powered inference (PPI) …

As artificial intelligence and machine learning tools become more accessible, and scientists
face new obstacles to data collection (eg rising costs, declining survey response rates) …

Learning the Individual Treatment Effect (ITE) is essential for personalized decision making,
yet causal inference has traditionally focused on aggregated treatment effects. While …

In modern data analysis, it is common to use machine learning methods to predict outcomes
on unlabelled datasets and then use these pseudo‐outcomes in subsequent statistical …

Machine learning models are increasingly used to produce predictions that serve as input
data in subsequent statistical analyses. For example, computer vision predictions of …

Abstract Summary ipd is an open-source R software package for the downstream modeling
of an outcome and its associated features where a potentially sizable portion of the outcome …