Cargando…
Causal inference in Python /
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 causa...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc.,
[2023]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | OR_on1390712589 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 230718s2023 caua o 001 0 eng d | ||
| 040 | |a ORMDA |b eng |e rda |e pn |c ORMDA |d EBLCP |d OCLCQ |d OCLCO | ||
| 020 | |z 9781098140250 | ||
| 035 | |a (OCoLC)1390712589 | ||
| 037 | |a 9781098140243 |b O'Reilly Media | ||
| 050 | 4 | |a Q325.5 | |
| 082 | 0 | 4 | |a 006.3/1 |2 23/eng/20230718 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Facure, Matheus, |e author. | |
| 245 | 1 | 0 | |a Causal inference in Python / |c by Matheus Facure. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, Inc., |c [2023] | |
| 300 | |a 1 online resource (406 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a Cover -- Copyright -- Table of Contents -- Preface -- Prerequisites -- Outline -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Part I. Fundamentals -- Chapter 1. Introduction to Causal Inference -- What Is Causal Inference? -- Why We Do Causal Inference -- Machine Learning and Causal Inference -- Association and Causation -- The Treatment and the Outcome -- The Fundamental Problem of Causal Inference -- Causal Models -- Interventions -- Individual Treatment Effect -- Potential Outcomes | |
| 505 | 8 | |a Consistency and Stable Unit Treatment Values -- Causal Quantities of Interest -- Causal Quantities: An Example -- Bias -- The Bias Equation -- A Visual Guide to Bias -- Identifying the Treatment Effect -- The Independence Assumption -- Identification with Randomization -- Key Ideas -- Chapter 2. Randomized Experiments and Stats Review -- Brute-Force Independence with Randomization -- An A/B Testing Example -- The Ideal Experiment -- The Most Dangerous Equation -- The Standard Error of Our Estimates -- Confidence Intervals -- Hypothesis Testing -- Null Hypothesis -- Test Statistic -- p-values | |
| 505 | 8 | |a Power -- Sample Size Calculation -- Key Ideas -- Chapter 3. Graphical Causal Models -- Thinking About Causality -- Visualizing Causal Relationships -- Are Consultants Worth It? -- Crash Course in Graphical Models -- Chains -- Forks -- Immorality or Collider -- The Flow of Association Cheat Sheet -- Querying a Graph in Python -- Identification Revisited -- CIA and the Adjustment Formula -- Positivity Assumption -- An Identification Example with Data -- Confounding Bias -- Surrogate Confounding -- Randomization Revisited -- Selection Bias -- Conditioning on a Collider | |
| 505 | 8 | |a Adjusting for Selection Bias -- Conditioning on a Mediator -- Key Ideas -- Part II. Adjusting for Bias -- Chapter 4. The Unreasonable Effectiveness of Linear Regression -- All You Need Is Linear Regression -- Why We Need Models -- Regression in A/B Tests -- Adjusting with Regression -- Regression Theory -- Single Variable Linear Regression -- Multivariate Linear Regression -- Frisch-Waugh-Lovell Theorem and Orthogonalization -- Debiasing Step -- Denoising Step -- Standard Error of the Regression Estimator -- Final Outcome Model -- FWL Summary -- Regression as an Outcome Model | |
| 505 | 8 | |a Positivity and Extrapolation -- Nonlinearities in Linear Regression -- Linearizing the Treatment -- Nonlinear FWL and Debiasing -- Regression for Dummies -- Conditionally Random Experiments -- Dummy Variables -- Saturated Regression Model -- Regression as Variance Weighted Average -- De-Meaning and Fixed Effects -- Omitted Variable Bias: Confounding Through the Lens of Regression -- Neutral Controls -- Noise Inducing Control -- Feature Selection: A Bias-Variance Trade-Off -- Key Ideas -- Chapter 5. Propensity Score -- The Impact of Management Training -- Adjusting with Regression | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 655 | 0 | |a Electronic books. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781098140243/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL30649969 | ||
| 994 | |a 92 |b IZTAP | ||