by Matheus Facure
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
