Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science
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Interesting in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains.
Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science
Much of our scientific inference revolves around the identification and replication of patterns in data. So what can be done when N=1? Eric Daza gives us a statistician's perspective on the ideas behind N-of-1 studies, its best examples, and strongest critiques.
0:00 - The purpose of N-of-1 & generalizability
3:30 - Successes and challenges in N-of-1
9:30 - A lightbulb moment
18:00 – Anomalies, Compliance, & Recurring Patterns
23:00 – Best Critiques of N-of-1, Safety, Efficacy
41:20 - Causal Inference
54:30 – Increasing the number of data scientists
1:03:30 – Biostatistics’ changing place in data science / statistical thinking
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