Behavioral Data Analysis with R and Python Customer Driven Data for Real Business Results 1st Edition by Florent Buisson ISBN 9781492061373 1492061379

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Product details:

ISBN 10: 1492061379
ISBN 13: 9781492061373
Author: Florent Buisson

Behavioral Data Analysis with R and Python Customer Driven Data for Real Business Results 1st Edition Table of contents:

I. Understanding Behaviors

1. The Causal-Behavioral Framework for Data Analysis

Why We Need Causal Analytics to Explain Human Behavior

The Different Types of Analytics

Human Beings Are Complicated

Confound It! The Hidden Dangers of Letting Regression Sort It Out

Data

Why Correlation Is Not Causation: A Confounder in Action

Too Many Variables Can Spoil the Broth

Conclusion

2. Understanding Behavioral Data

A Basic Model of Human Behavior

Personal Characteristics

Cognition and Emotions

Intentions

Actions

Business Behaviors

How to Connect Behaviors and Data

Develop a Behavioral Integrity Mindset

Distrust and Verify

Identify the Category

Refine Behavioral Variables

Understand the Context

Conclusion

II. Causal Diagrams and Deconfounding

3. Introduction to Causal Diagrams

Causal Diagrams and the Causal-Behavioral Framework

Causal Diagrams Represent Behaviors

Causal Diagrams Represent Data

Fundamental Structures of Causal Diagrams

Chains

Forks

Colliders

Common Transformations of Causal Diagrams

Slicing/Disaggregating Variables

Aggregating Variables

What About Cycles?

Paths

Conclusion

4. Building Causal Diagrams from Scratch

Business Problem and Data Setup

Data and Packages

Understanding the Relationship of Interest

Identify Candidate Variables to Include

Actions

Intentions

Cognition and Emotions

Personal Characteristics

Business Behaviors

Time Trends

Validate Observable Variables to Include Based on Data

Relationships Between Numeric Variables

Relationships Between Categorical Variables

Relationships Between Numeric and Categorical Variables

Expand Causal Diagram Iteratively

Identify Proxies for Unobserved Variables

Identify Further Causes

Iterate

Simplify Causal Diagram

Conclusion

5. Using Causal Diagrams to Deconfound Data Analyses

Business Problem: Ice Cream and Bottled Water Sales

The Disjunctive Cause Criterion

Definition

First Block

Second Block

The Backdoor Criterion

Definitions

First Block

Second Block

Conclusion

III. Robust Data Analysis

6. Handling Missing Data

Data and Packages

Visualizing Missing Data

Amount of Missing Data

Correlation of Missingness

Diagnosing Missing Data

Causes of Missingness: Rubin’s Classification

Diagnosing MCAR Variables

Diagnosing MAR Variables

Diagnosing MNAR Variables

Missingness as a Spectrum

Handling Missing Data

Introduction to Multiple Imputation (MI)

Default Imputation Method: Predictive Mean Matching

From PMM to Normal Imputation (R Only)

Adding Auxiliary Variables

Scaling Up the Number of Imputed Data Sets

Conclusion

7. Measuring Uncertainty with the Bootstrap

Intro to the Bootstrap: “Polling” Oneself Up

Packages

The Business Problem: Small Data with an Outlier

Bootstrap Confidence Interval for the Sample Mean

Bootstrap Confidence Intervals for Ad Hoc Statistics

The Bootstrap for Regression Analysis

When to Use the Bootstrap

Conditions for the Traditional Central Estimate to Be Sufficient

Conditions for the Traditional CI to Be Sufficient

Determining the Number of Bootstrap Samples

Optimizing the Bootstrap in R and Python

R: The BehavioralDataAnalysis Package

Python Optimization

Conclusion

IV. Designing and Analyzing Experiments

8. Experimental Design: The Basics

Planning the Experiment: Theory of Change

Business Goal and Target Metric

Intervention

Behavioral Logic

Data and Packages

Determining Random Assignment and Sample Size/Power

Random Assignment

Sample Size and Power Analysis

Analyzing and Interpreting Experimental Results

Conclusion

9. Stratified Randomization

Planning the Experiment

Business Goal and Target Metric

Definition of the Intervention

Behavioral Logic

Data and Packages

Determining Random Assignment and Sample Size/Power

Random Assignment

Power Analysis with Bootstrap Simulations

Analyzing and Interpreting Experimental Results

Intention-to-Treat Estimate for Encouragement Intervention

Complier Average Causal Estimate for Mandatory Intervention

Conclusion

10. Cluster Randomization and Hierarchical Modeling

Planning the Experiment

Business Goal and Target Metric

Definition of the Intervention

Behavioral Logic

Data and Packages

Introduction to Hierarchical Modeling

R Code

Python Code

Determining Random Assignment and Sample Size/Power

Random Assignment

Power Analysis

Analyzing the Experiment

Conclusion

V. Advanced Tools in Behavioral Data Analysis

11. Introduction to Moderation

Data and Packages

Behavioral Varieties of Moderation

Segmentation

Interactions

Nonlinearities

How to Apply Moderation

When to Look for Moderation?

Multiple Moderators

Validating Moderation with Bootstrap

Interpreting Individual Coefficients

Conclusion

12. Mediation and Instrumental Variables

Mediation

Understanding Causal Mechanisms

Causal Biases

Identifying Mediation

Measuring Mediation

Instrumental Variables

Data

Packages

Understanding and Applying IVs

Measurement

Applying IVs: Frequently Asked Questions

Conclusion

Bibliography

Index

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Tags: Florent Buisson, Behavioral Data Analysis, Customer Driven Data