book

Behavioral Data Analysis with R and Python

Name: Behavioral Data Analysis with R and Python
Author: Florent Buisson
ISBN: 9781492061373

by Florent Buisson

June 2021

Beginner to intermediate

358 pages

10h

English

O'Reilly Media, Inc.

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Preface
Who This Book Is ForWho This Book Is Not ForR and Python CodeCode EnvironmentsCode ConventionsFunctional-Style Programming 101Using Code ExamplesNavigating This BookConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
I. Understanding Behaviors
1. The Causal-Behavioral Framework for Data Analysis
Why We Need Causal Analytics to Explain Human BehaviorThe Different Types of AnalyticsHuman Beings Are ComplicatedConfound It! The Hidden Dangers of Letting Regression Sort It OutDataWhy Correlation Is Not Causation: A Confounder in ActionToo Many Variables Can Spoil the BrothConclusion
2. Understanding Behavioral Data
A Basic Model of Human BehaviorPersonal CharacteristicsCognition and EmotionsIntentionsActionsBusiness BehaviorsHow to Connect Behaviors and DataDevelop a Behavioral Integrity MindsetDistrust and VerifyIdentify the CategoryRefine Behavioral VariablesUnderstand the ContextConclusion
II. Causal Diagrams and Deconfounding
3. Introduction to Causal Diagrams
Causal Diagrams and the Causal-Behavioral FrameworkCausal Diagrams Represent BehaviorsCausal Diagrams Represent DataFundamental Structures of Causal DiagramsChainsForksCollidersCommon Transformations of Causal DiagramsSlicing/Disaggregating VariablesAggregating VariablesWhat About Cycles?PathsConclusion
4. Building Causal Diagrams from Scratch
Business Problem and Data SetupData and PackagesUnderstanding the Relationship of InterestIdentify Candidate Variables to IncludeActionsIntentionsCognition and EmotionsPersonal CharacteristicsBusiness BehaviorsTime TrendsValidate Observable Variables to Include Based on DataRelationships Between Numeric VariablesRelationships Between Categorical VariablesRelationships Between Numeric and Categorical VariablesExpand Causal Diagram IterativelyIdentify Proxies for Unobserved VariablesIdentify Further CausesIterateSimplify Causal DiagramConclusion
5. Using Causal Diagrams to Deconfound Data Analyses
Business Problem: Ice Cream and Bottled Water SalesThe Disjunctive Cause CriterionDefinitionFirst BlockSecond BlockThe Backdoor CriterionDefinitionsFirst BlockSecond BlockConclusion
III. Robust Data Analysis
6. Handling Missing Data
Data and PackagesVisualizing Missing DataAmount of Missing DataCorrelation of MissingnessDiagnosing Missing DataCauses of Missingness: Rubin’s ClassificationDiagnosing MCAR VariablesDiagnosing MAR VariablesDiagnosing MNAR VariablesMissingness as a SpectrumHandling Missing DataIntroduction to Multiple Imputation (MI)Default Imputation Method: Predictive Mean MatchingFrom PMM to Normal Imputation (R Only)Adding Auxiliary VariablesScaling Up the Number of Imputed Data SetsConclusion

7. Measuring Uncertainty with the Bootstrap
Intro to the Bootstrap: “Polling” Oneself UpPackagesThe Business Problem: Small Data with an OutlierBootstrap Confidence Interval for the Sample MeanBootstrap Confidence Intervals for Ad Hoc StatisticsThe Bootstrap for Regression AnalysisWhen to Use the BootstrapConditions for the Traditional Central Estimate to Be SufficientConditions for the Traditional CI to Be SufficientDetermining the Number of Bootstrap SamplesOptimizing the Bootstrap in R and PythonR: The BehavioralDataAnalysis PackagePython OptimizationConclusion
IV. Designing and Analyzing Experiments
8. Experimental Design: The Basics
Planning the Experiment: Theory of ChangeBusiness Goal and Target MetricInterventionBehavioral LogicData and PackagesDetermining Random Assignment and Sample Size/PowerRandom AssignmentSample Size and Power AnalysisAnalyzing and Interpreting Experimental ResultsConclusion
9. Stratified Randomization
Planning the ExperimentBusiness Goal and Target MetricDefinition of the InterventionBehavioral LogicData and PackagesDetermining Random Assignment and Sample Size/PowerRandom AssignmentPower Analysis with Bootstrap SimulationsAnalyzing and Interpreting Experimental ResultsIntention-to-Treat Estimate for Encouragement InterventionComplier Average Causal Estimate for Mandatory InterventionConclusion
10. Cluster Randomization and Hierarchical Modeling
Planning the ExperimentBusiness Goal and Target MetricDefinition of the InterventionBehavioral LogicData and PackagesIntroduction to Hierarchical ModelingR CodePython CodeDetermining Random Assignment and Sample Size/PowerRandom AssignmentPower AnalysisAnalyzing the ExperimentConclusion
V. Advanced Tools in Behavioral Data Analysis
11. Introduction to Moderation
Data and PackagesBehavioral Varieties of ModerationSegmentationInteractionsNonlinearitiesHow to Apply ModerationWhen to Look for Moderation?Multiple ModeratorsValidating Moderation with BootstrapInterpreting Individual CoefficientsConclusion
12. Mediation and Instrumental Variables
MediationUnderstanding Causal MechanismsCausal BiasesIdentifying MediationMeasuring MediationInstrumental VariablesDataPackagesUnderstanding and Applying IVsMeasurementApplying IVs: Frequently Asked QuestionsConclusion
Bibliography
Index
About the Author

Content preview from Behavioral Data Analysis with R and Python

About the Author

Florent Buisson is a behavioral economist with more than 10 years of experience in business, analytics, and behavioral science. He currently leads the experimentation practice of the online marketplace Cars.com.

He previously worked for a French strategy consulting firm where he used economics and data analysis to answer complex measurement questions, for example building an index measuring the stability of agricultural policies in developing countries on behalf of the UN Food and Agriculture Organization. He also started and led for four years the behavioral science team at Allstate Insurance Company.

Florent has published academic articles in journals such as the peer-reviewed Journal of Real Estate Research. He holds a master’s degree in econometrics as well as a PhD in behavioral economics from the Sorbonne University in Paris.

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Behavioral Data Analysis with R and Python

by Florent Buisson

About the Author

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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