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, ...

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 ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

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 ...

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

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.