Causal Inference in Python: Applying Causal Inference in the Tech Industry

How many buyers will an additional dollar of online marketing  bring in? Which customers will only buy when given a discount coupon?  How do you establish an optimal pricing strategy? The best way to  determine how the levers at our disposal affect the business metrics we  want to drive is through causal inference. 

 In this  book, author Matheus Facure, senior data scientist at Nubank, explains  the largely untapped potential of causal inference for estimating  impacts and effects. Managers, data scientists, and business analysts  will learn classical causal inference methods like randomized control  trials (A/B tests), linear regression, propensity score, synthetic  controls, and difference-in-differences. Each method is accompanied by  an application in the industry to serve as a grounding example. 

 With this book, you will: 

• Learn how to use basic concepts of causal inference 
• Frame a business problem as a causal inference problem 
• Understand how bias gets in the way of causal inference 
• Learn how causal effects can differ from person to person 
• Use repeated observations of the same customers across time to adjust for biases 
• Understand how causal effects differ across geographic locations 
• Examine noncompliance bias and effect dilution