000			05371cam a22005057i 4500
001			21050
003			OSt
005			20241208140755.0
008			240326t20232023caua 001 0 eng d
020			_a9781098140250 _q(paperback)
020			_a1098140257 _q(paperback)
040			_aOKU _beng _erda _cOKU _dOCLCO _dGZN _dOCLCO _dCDX _dOCLCF _dDLC _dIQ-MoCLU
082	0	4	_a005.13/3 _223 _bF143
100	1		_aFacure, Matheus, _eauthor.
245	1	0	_aCausal inference in Python : _bapplying causal inference in the tech industry / _cMatheus Facure.
250			_aFirst edition.
264		1	_aSebastopol, CA : _bO'Reilly Media, Inc., _c2023.
264		4	_c©2023
300			_axix, 385 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIncludes index.
505	0		_aCover -- 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		_aConsistency 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		_aPower -- 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		_aAdjusting 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		_aPositivity 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.
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"--
650		0	_aPython (Computer program language)
650		0	_aCausation _xData processing.
650		0	_aInference _xData processing.
650		0	_aHigh technology industries _xData processing.
650		0	_aMachine learning.
650		6	_aApprentissage automatique.
650		6	_aPython (Langage de programmation)
650		6	_aInférence (Logique) _xInformatique.
650		6	_aIndustries de pointe _xInformatique.
650		7	_aInference _xData processing. _2fast
650		7	_aMachine learning. _2fast
650		7	_aPython (Computer program language) _2fast
910			_aSAJA
942			_2ddc _cBK
999			_c21050 _d21050