Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...

Recently, a research team from the Hong Kong University of Science and Technology (Guangzhou) and Tencent published findings ...

We describe how ordinary interpretations of causal models and causal graphs fail to capture important distinctions among ignorable allocation mechanisms for subject selection or allocation. We ...

Bernd Greifeneder is the CTO and Founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. IT, development and business departments are under more ...

We present results that allow the researcher in certain cases to determine the direction of the bias that arises when control for confounding is inadequate. The results are given within the context of ...

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 ...

Alembic is the first to eliminate the guesswork in calculating marketing ROI using composite AI, a graph neural network and contact-tracing mathematics developed during the pandemic SAN ...

Joint efforts kick off with DREAM Challenge that allows scientists to compute on NIH’s Covid data in a privacy-enhancing environment and experience an unprecedented level of transparency in generative ...