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在线营销多投入一美元会带来多少买家？哪些客户只有在获得折扣券时才会购买？如何建立最佳定价策略？确定我们手头的杠杆如何影响我们想要推动的业务指标的最佳方法是通过因果推断。
在本书中，Nubank高级数据科学家Matheus Facure解释了因果推断估计影响和效果的潜力。经理、数据科学家和业务分析师将学习经典的因果推断方法，如随机对照试验（A/B测试）、线性回归、倾向得分、合成控制和差异法。每种方法都附有一个行业应用示例，作为基础示例。
通过本书，您将：
- 学习如何使用因果推断的基本概念
- 将业务问题框架化为因果推断问题
- 了解偏见如何妨碍因果推断
- 了解因果效应如何因人而异
- 使用同一客户在时间上的重复观察来调整偏差
- 了解因果效应在地理位置上的差异
- 检查不遵从偏见和效应稀释
Book description
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
- [url=]Causal Inference in Python.epu ...[/url]
- [url=][url=]Causal Inference in Python.pdf[/url][/url]
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