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, ...
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 the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...
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.
NTT has developed a new inference framework designed to improve the transparency and reliability of large vision-language ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results