Sinossi
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
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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Causal Inference in Python: Applying Causal Inference in the Tech Industry
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O'Reilly Media, Sebastopol, 2023
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Paperback. Condizione: new. 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 About the Author Matheus Facure is an Economist and Senior Data Scientist at Nubank, the biggest FinTech company outside Asia. His has successfully applied causal inference in a wide range of business scenarios, from automated and real time interest and credit decision making, to cross sell emails and optimizing marketing budgets. He is also author of Causal Inference for the Brave and True, a popular book which aims at making causal inference mainstream in a light-hearted, yet rigorous way." In this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781098140250
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Causal Inference in Python: Applying Causal Inference in the Tech Industry [Paperback] Facure, Matheus
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Paperback. Condizione: New. 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 inferenceFrame a business problem as a causal inference problemUnderstand how bias gets in the way of causal inferenceLearn how causal effects can differ from person to personUse repeated observations of the same customers across time to adjust for biasesUnderstand how causal effects differ across geographic locationsExamine noncompliance bias and effect dilution. Codice articolo LU-9781098140250
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