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Harnessing temporal information to determine causal relationships in data analysis. Explore Granger causality and its applications in economic phenomena like wage and price inflation. Learn to model and analyze observational data effectively.
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Establishing Cause and Effect from Data Jason McFall, Causata
Use temporal information Causes precede effects
Granger causality Does wage inflation lead to price inflation, or vice versa? U W P X
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Analysing observational data is hard Bayesian networks D G Im IR BP U T X P X W
Experiment Put the science into data science
Randomised Controlled Trials INTERVENTION Measure outcomes for both groups Split population randomly into two groups CONTROL
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Bandit algorithms Including random exploration
Regulated exploration/exploitation response rate
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Beware of bias Credit: Flickr winnifredxoxo
Summary Analysing observational data is HARD. It’s often much easier to do experiments! Randomise Use concurrent controls Be alert to bias 1 2 3 4