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 user
- 3:47 How does Causal Impact work?
- 5:45 Control group and uplift
- 7:05 How does the BSTS model work?
- 10:24 What is a prior in bayesian statistics?
- 11:25 Evaluating prediction accuracy
- 14:28 Shortcomings of Causal Impact
- 21:18 Causal Impact resources and background
Quotes:
(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:
