Causal inference in observational settings seeks to estimate the effect of exposures, treatments or interventions on outcomes in the absence of random assignment. Unlike experimental designs, ...

Scientific ideas sometimes have to wait decades for technology to catch up. Statistical algorithms developed at the Yerevan ...

At the core of science is a commitment to rigorous reasoning, method, and the use of evidence. The final session of the workshop was designed to take a step back from the specific issues of how ...

Causal inference methods are central to determining the true impact of policies in fields such as public health, education, taxation and environmental regulation. Whereas randomised controlled trials ...

Researchers at Texas Children's Neurological Research Institute (NRI) and Baylor College of Medicine have developed a powerful new tool within the Genome Aggregation Database (gnomAD) to sharpen the ...

Neutron sources can be directly identified from measured spectra rather than proxies using inference tools adapted from ...

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

A lot of ink has been spilled on the question of what will ultimately win: the scientific method, an approach to learning about the world by coming up with theories and testing those theories against ...