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Establishing Cause and Effect from Data

Understand the temporal information from data to determine cause and effect relationships. Explore Granger causality to determine if wage inflation leads to price inflation or vice versa. Learn how to build models, analyze observational data, and conduct experiments using Bayesian networks and Randomized Controlled Trials. Avoid bias and explore bandit algorithms for response rate optimization. Click to acquire new customers and retain existing ones effectively.

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Establishing Cause and Effect from Data

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  1. Establishing Cause and Effect from Data Jason McFall, Causata

  2. Use temporal information Causes precede effects

  3. Granger causality Does wage inflation lead to price inflation, or vice versa? U W P X

  4. Store all the detail, in time order WebsiteSession Call Center Question WebsiteSession WebsiteSession Loyalty Card Promo Email Loyalty Card Sign Up Major Product Purchase in Store

  5. time filter customer journeys

  6. before after relative time time align filter customer journeys

  7. Build models

  8. Analysing observational data is hard Bayesian networks D G Im IR BP U T X P X W

  9. Experiment Put the science into data science

  10. Randomised Controlled Trials INTERVENTION Measure outcomes for both groups Split population randomly into two groups CONTROL

  11. SAVE 25% RENEW vs Renew your 12 monthvirus protection your 12 monthvirus protection Renewal rate

  12. Randomise

  13. Concurrent control

  14. badscience.net

  15. Bandit algorithms Including random exploration

  16. Regulated exploration/exploitation response rate

  17. LoremIpsum

  18. Measure the right thing! Click on ad • Acquire new customer • Retain Customer

  19. Beware of bias Credit: Flickr winnifredxoxo

  20. Beware of bias

  21. Confirmation bias

  22. 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

  23. Thankyou

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