Nature

Causal Inference Using Bayesian Networks

Bayesian networks are probabilistic graphical models that encode conditional dependencies among variables within a directed acyclic graph. In the context of causal inference, these networks provide a ...
Diginomica

How causal analysis and AI intersect - methods of causal inference

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 ...
BMJ

Causal inference in physical activity epidemiology research: an opportunity for better evidence

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
Times Higher Education

Research Engineer (Federated Causal Inference in Heterogeneous Data Environments) - UP

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are ...
techtimes

LLM Data Mixture Breaks When Training Pools Shift: Causal Inference Offers Fix

Every large language model begins as a question of proportion. How much web text? How much code? How much scientific literature? The ratio of training data across these domains — what researchers call ...