Show notes
Alicia Horsch is a Data Scientist in the Marketing and Analytics teams at Social Point, a mobile game developer based in Barcelona. She is also an ambassador for Women in Data, a nonprofit organization that focuses on increasing diversity in data careers. Questions Alicia Answered in this Episode:What is Causal Impact and why do you want to use it?What’s so special about using offline campaigns as far as Causal Impact?How does Causal Impact work?How do you split traffic into the treatment group and the control group?Would you like to elaborate on the math behind it and how the model is built?How do you know if the predictions are good?What are the shortcomings of the Causal Impact package?What are the most important things that you look for in a dimension to split the events on?What resources can you recommend to our listeners who want to learn more about Causal Impact?Timestamp:1:25 What is Causal Impact?2:22 Causal Impact for when you can’t track the user3:47 How does Causal Impact work?5:45 Control group and uplift7:05 How does the BSTS model work?10:24 What is a prior in bayesian statistics?11:25 Evaluating prediction accuracy14:28 Shortcomings of Causal Impact21:18 Causal Impact resources and backgroundQuotes:(3:49 - 4:04) "Causal impact works by using some information to make a prediction on what would've happened if there wouldn't have been a marketing campaign, which is also often called the counterfactual."Mentioned in this Episode:Alicia Horsch’s LinkedInSocial PointSocial Point’s career websiteWomen in DataThe CausalImpact package
