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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Facure, Matheus (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc., [2023]
Edición:[First edition].
Temas:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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