Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...

A new framework called FLARE combines nonlinear machine learning flexibility with closed-form uncertainty estimates, ...

Researchers have developed MRZ, a new Mendelian randomization method that robustly classifies genetic instruments to deliver ...

Multivariate statistical inference encompasses techniques for analysing and drawing conclusions from data in which multiple interrelated variables are observed simultaneously. Unlike univariate ...

Statistical inference in linear models centres on estimating relationships between a response variable and one or more predictors under the assumption that these relationships can be expressed as a ...

In the 21st century, artificial intelligence (AI) has emerged as a valuable approach in data science and a growing influence in medical research, 4-6 with an accelerating pace of innovation. This ...

Big Data—broadly considered as datasets whose size, complexity, and heterogeneity preclude conventional approaches to storage and analysis—continues to generate interest across many scientific domains ...

"Since 2020, rural real estate prices have been rising"—this 'observation' is correct. However, the 'causality' that ...

All of the captive kea were given the opportunity to participate in the sampling task, but not all of them were interested. (Credit: Amalia Bastos.) Those remarkable kea are at it again: now the ...

Eleanor has an undergraduate degree in zoology from the University of Reading and a master’s in wildlife documentary production from the University of Salford.View full profile Eleanor has an ...