Cause & Effect
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Description
Remember back at school when you were taught that correlation doesn’t mean causation, that increased ice cream sales are correlated with sunnier weather but don’t cause the clouds to part? Peter Tennant, a fellow of the Alan Turing Institute based at Leeds Institute for Data Analytics explains why it’s important for scientists to become more confident in talking about causation, how "causal inference" methods are transforming the field of epidemiology and why AI isn’t typically best placed to make sensible assumptions about complex data. This episode was recorded before the Covid-19 lockdown began in the UK, but the topics discussed couldn’t be more relevant!
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