Correlation is not Causation! But how can we find answers to questions like "How effective is a given treatment in preventing a disease" or "Did global warming cause this heat wave" based on available ...
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 ...
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, ...
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 ...
Bristol Medical School offer a comprehensive programme of live online short courses, run by subject experts. All courses grant continued access to the course materials and recordings for 5 months ...
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