Tech Talk - Causal Inference in Data Science From Prediction to Causation

Key Notes

• Games Prediction - Higher Activity logins, Friends 
• It could be another way, They play games and make friends
• How do we increase the activity?
• Different segments of people - Games to Friends, Ask Friends and play games
• Observational metrics - Be mindful of hidden causes

• Measure versions of algos - A/B Testing
• Impact of Algo on different types of people
• Lower activity - Higher CTR
• CTR for different segments of users
• Segment people and see the behavior of each segment with experiments
• Combination of Experiments / Conversions / Measure of it
• ML Recommendations
• Split groups into different selections of the same category
• The choice for new Algos - Frequent Buyers
• Choice of old Algos - Low-frequency Buyers
• Purchase behavior trend over years
• Purchase behavior of new buyers
• Experiment - Conversions - Alerts (Forecast vs Actuals)

Frameworks

• Causal graphical models
• Potential outcome framework

• What would have happened if you did that?
• What would have happened if you had not done that?

Evaluate existing systems

• Old recommendations vs New Recommendation
• Measure forecast deviations against actuals qualitatively

Feedback loop informs current best algo!!!

Keep Thinking!!!