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Do We Need Probability?
Causal inference lies at the very heart of the scientific method.
Randomized controlled trials (RCTs; also known as randomized experiemnts or A/B tests) are often called "the golden standard for causal inference".
It's a less known fact that randomized trials have their limitations in answering causal questions.
What are the most common myths about randomization?
What causal questions can and cannot be answered with randomized experiments? Finally, why do we need probability?
Join me on a fascinating journey into clinical trials, randomization and generalization.
Ready to meet Stephen Senn?
About The Guest
Stephen Senn, PhD, is a statistician and consultant specializing in clinical trials for drug development. He is a former Group Head at Ciba-Geigy and has served as a professor at the University of Glasgow and University College London (UCL). He is the author of "Statistical Issues in Drug Development," "Crossover Trials in Clinical Research," and "Dicing with Death".
Connect with Stephen:
- Stephen on Twitter/X
- Stephen on LinkedIn
- Stephen's web page
About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.
Connect with Alex:
- Alex on the Internet
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Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
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