Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results

 Copyright....6 Table of Contents....7 Preface....13 Who This Book Is For....14 Who This Book Is Not For....15 R and Python Code....16 Code Environments....16 Code Conventions....17 Functional-Style Programming 101....18 Using Code Examples....18 Navigating This Book....19 Conventions Used in This Book....20 O’Reilly Online Learning....21 How to Contact Us....21 Acknowledgments....22 Part I. Understanding Behaviors....23 Chapter 1. The Causal-Behavioral Framework for Data Analysis....25 Why We Need Causal Analytics to Explain Human Behavior....26 The Different Types of Analytics....26 Human Beings Are Complicated....27 Confound It! The Hidden Dangers of Letting Regression Sort It Out....30 Data....31 Why Correlation Is Not Causation: A Confounder in Action....31 Too Many Variables Can Spoil the Broth....33 Conclusion....39 Chapter 2. Understanding Behavioral Data....41 A Basic Model of Human Behavior....42 Personal Characteristics....43 Cognition and Emotions....45 Intentions....46 Actions....47 Business Behaviors....48 How to Connect Behaviors and Data....50 Develop a Behavioral Integrity Mindset....50 Distrust and Verify....51 Identify the Category....52 Refine Behavioral Variables....54 Understand the Context....55 Conclusion....57 Part II. Causal Diagrams and Deconfounding....59 Chapter 3. Introduction to Causal Diagrams....61 Causal Diagrams and the Causal-Behavioral Framework....62 Causal Diagrams Represent Behaviors....63 Causal Diagrams Represent Data....64 Fundamental Structures of Causal Diagrams....68 Chains....68 Forks....72 Colliders....74 Common Transformations of Causal Diagrams....75 Slicing/Disaggregating Variables....76 Aggregating Variables....77 What About Cycles?....79 Paths....82 Conclusion....83 Chapter 4. Building Causal Diagrams from Scratch....85 Business Problem and Data Setup....86 Data and Packages....86 Understanding the Relationship of Interest....87 Identify Candidate Variables to Include....89 Actions....91 Intentions....92 Cognition and Emotions....93 Personal Characteristics....94 Business Behaviors....97 Time Trends....97 Validate Observable Variables to Include Based on Data....99 Relationships Between Numeric Variables....100 Relationships Between Categorical Variables....103 Relationships Between Numeric and Categorical Variables....106 Expand Causal Diagram Iteratively....108 Identify Proxies for Unobserved Variables....108 Identify Further Causes....109 Iterate....110 Simplify Causal Diagram....110 Conclusion....112 Chapter 5. Using Causal Diagrams to Deconfound Data Analyses....113 Business Problem: Ice Cream and Bottled Water Sales....114 The Disjunctive Cause Criterion....116 Definition....116 First Block....117 Second Block....119 The Backdoor Criterion....119 Definitions....120 First Block....122 Second Block....123 Conclusion....125 Part III. Robust Data Analysis....127 Chapter 6. Handling Missing Data....129 Data and Packages....131 Visualizing Missing Data....132 Amount of Missing Data....135 Correlation of Missingness....137 Diagnosing Missing Data....143 Causes of Missingness: Rubin’s Classification....146 Diagnosing MCAR Variables....148 Diagnosing MAR Variables....150 Diagnosing MNAR Variables....152 Missingness as a Spectrum....154 Handling Missing Data....158 Introduction to Multiple Imputation (MI)....159 Default Imputation Method: Predictive Mean Matching....162 From PMM to Normal Imputation (R Only)....163 Adding Auxiliary Variables....165 Scaling Up the Number of Imputed Data Sets....167 Conclusion....168 Chapter 7. Measuring Uncertainty with the Bootstrap....169 Intro to the Bootstrap: “Polling” Oneself Up....170 Packages....170 The Business Problem: Small Data with an Outlier....170 Bootstrap Confidence Interval for the Sample Mean....172 Bootstrap Confidence Intervals for Ad Hoc Statistics....177 The Bootstrap for Regression Analysis....179 When to Use the Bootstrap....182 Conditions for the Traditional Central Estimate to Be Sufficient....183 Conditions for the Traditional CI to Be Sufficient....183 Determining the Number of Bootstrap Samples....186 Optimizing the Bootstrap in R and Python....187 R: The boot Package....187 Python Optimization....190 Conclusion....191 Part IV. Designing and Analyzing Experiments....193 Chapter 8. Experimental Design: The Basics....195 Planning the Experiment: Theory of Change....196 Business Goal and Target Metric....197 Intervention....199 Behavioral Logic....201 Data and Packages....203 Determining Random Assignment and Sample Size/Power....204 Random Assignment....204 Sample Size and Power Analysis....207 Analyzing and Interpreting Experimental Results....221 Conclusion....224 Chapter 9. Stratified Randomization....225 Planning the Experiment....227 Business Goal and Target Metric....227 Definition of the Intervention....229 Behavioral Logic....230 Data and Packages....230 Determining Random Assignment and Sample Size/Power....231 Random Assignment....231 Power Analysis with Bootstrap Simulations....239 Analyzing and Interpreting Experimental Results....246 Intention-to-Treat Estimate for Encouragement Intervention....247 Complier Average Causal Estimate for Mandatory Intervention....248 Conclusion....254 Chapter 10. Cluster Randomization and Hierarchical Modeling....255 Planning the Experiment....256 Business Goal and Target Metric....256 Definition of the Intervention....256 Behavioral Logic....258 Data and Packages....258 Introduction to Hierarchical Modeling....259 R Code....260 Python Code....262 Determining Random Assignment and Sample Size/Power....264 Random Assignment....264 Power Analysis....266 Analyzing the Experiment....274 Conclusion....274 Part V. Advanced Tools in Behavioral Data Analysis....277 Chapter 11. Introduction to Moderation....279 Data and Packages....280 Behavioral Varieties of Moderation....280 Segmentation....281 Interactions....287 Nonlinearities....288 How to Apply Moderation....291 When to Look for Moderation?....292 Multiple Moderators....303 Validating Moderation with Bootstrap....309 Interpreting Individual Coefficients....311 Conclusion....317 Chapter 12. Mediation and Instrumental Variables....319 Mediation....320 Understanding Causal Mechanisms....320 Causal Biases....321 Identifying Mediation....323 Measuring Mediation....324 Instrumental Variables....329 Data....329 Packages....330 Understanding and Applying IVs....330 Measurement....333 Applying IVs: Frequently Asked Questions....336 Conclusion....337 Bibliography....339 Index....343 About the Author....359 Colophon....359