Causal Inference in Python: Applying Causal Inference in the Tech Industry (Paperback)

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

這本書的作者Matheus Facure是Nubank的高級數據科學家，他在書中解釋了因果推論在估計影響和效果方面的潛力。經理、數據科學家和業務分析師將學習到像隨機對照試驗（A/B測試）、線性回歸、傾向得分、合成對照和差異法等傳統因果推論方法。每種方法都附有業界應用的實例，作為基礎示例。

通過這本書，您將能夠：
- 學習如何使用因果推論的基本概念
- 將業務問題框架化為因果推論問題
- 了解偏差如何妨礙因果推論
- 學習因果效應如何因人而異
- 使用同一客戶在不同時間的重複觀察來調整偏差
- 了解因果效應在地理位置上的差異
- 檢查不遵從偏差和效果稀釋的問題