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

Matheus Facure

ISBN 10: 1098140257 / ISBN 13: 9781098140250

Editore: O'reilly Media Aug 2023, 2023

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Neuware - 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.

Codice articolo 9781098140250

Dati bibliografici

• Titolo: Causal Inference in Python : Applying Causal Inference in the Tech Industry
• Editore: O'reilly Media Aug 2023
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1098140257
• ISBN 13: 9781098140250
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 227 L x 192 A x 23 P (millimetri) 227 millimetri di larghezza per 192 millimetri di altezza per 23 millimetri di profondità
• Peso: 698 grammi

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Riassunto

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