Causal Inference in R : Decipher Complex Relationships with Advanced R Techniques for Data-Driven Decision-making / Subhajit Das.

Format:

Book

Author/Creator:

Das, Subhajit, author.

Language:

English

Subjects (All):

R (Computer program language).

Causation--Statistical methods.

Causation.

Regression analysis.

Decision making--Statistical methods.

Decision making.

Physical Description:

1 online resource (382 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2024]

System Details:

Mode of access: World Wide Web.

Biography/History:

Das Subhajit: Subhajit Das holds a PhD in computer science from Georgia Institute of Technology, USA, specializing in machine learning (ML) and visual analytics. With 10+ years of experience, he is an expert in causal inference, revealing complex relationships and data-driven decision-making. His work has influenced millions in AI, e-commerce, logistics, and 3D software sectors. He has collaborated with leading companies, such as Amazon, Microsoft, Bosch, UPS, 3M, and Autodesk, creating solutions that seamlessly integrate causal reasoning and ML. His research, published in top conferences, focuses on developing AI-powered interactive systems for domain experts. He also holds a master's degree in design computing from the University of Pennsylvania, USA.

Summary:

Determining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making. This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You’ll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You’ll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data. By the end of this book, you’ll be able to confidently establish causal relationships and make data-driven decisions with precision.

Contents:

Cover

Title Page

Copyright and Credits

Dedicated

Contributors

Table of Contents

Preface

Part 1: Foundations of Causal Inference

Chapter 1: Introducing Causal Inference

Defining causal inference

Historical perspective on causal inference

Why do we need causality?

Is it an association or really causation?

Deep dive causality in real-life settings

Exploring the technical aspects of causality

Simpson's paradox

Defining variables

Summary

References

Chapter 2: Unraveling Confounding and Associations

A deep dive into associations

Causality and a fundamental issue

Individual treatment effect

Average treatment effect

The distinction between confounding and associations

Discussing the concept of bias in causality

Assumptions in causal inference

Strategies to address confounding

Regression adjustment

Propensity score methods

Chapter 3: Initiating R with a Basic Causal Inference Example

Technical requirements

What is R? Why use R for causal inference?

Getting started with R

Setting up the R environment

Navigating the RStudio interface

Basic R programming concepts

Data types in R

Advanced data structures

Packages in R

Preparing for causal inference in R

Preparing and loading data

Exploratory data analysis (EDA)

Simple causal inference techniques

Comparing means (t-tests)

Regression analysis

Propensity score matching

Case study - a basic causal analysis in R

Data preparation and inspection

Understanding the data

Performing causal analysis

Part 2: Practical Applications and Core Methods

Chapter 4: Constructing Causality Models with Graphs

Basics of graph theory

Types of graphs - directed versus undirected.

Other graph typologies

Why we need DAGs in causal science

Graph representations of variables

Mathematical interpretation

Representing graphs in R

Bayesian networks

Conditional independence

Exploring Graphical Causal Models

Comparison with Bayesian networks

Assumptions in GCMs

Case study example of a graph model in R

Problem to solve using graphs

Implementing in R

Interpreting results

Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs

Understanding the flow in Graphs

Chains and forks

Colliders

Adjusting for confounding in graphs

D-separation

Do-operator

The back door adjustment

The front door adjustment

Practical R example - back door versus front door

Synthetic data

Back door adjustment in R

Front door adjustment in R

Chapter 6: Employing Propensity Score Techniques

Introduction to propensity scores

A deep dive into these scores

Balancing confounding variables

Check for confounding using propensity scores

Challenges and caveats

Stratification and subsampling

Theory

Application of propensity scores in R

Understanding Propensity Score Matching

Considerations and limitations

Practical application of PSM in R

Balancing methods

Sensitivity analysis

Visualizing the results

Weighting in PSM using R

Chapter 7: Employing Regression Approaches for Causal Inference

Role of regression in causality

Choosing the appropriate regression model

Understanding the nature of the outcome variable

Consideration of confounding and interaction effects

Model complexity, parsimony, and assumptions

Linear regression for causal inference

The theory.

Application of regression modeling in R

Single versus multivariate regression

Treatment orthogonalization

Example of the FWL theorem

Model diagnostics and assumptions

Non-linear regression for causal inference

Other types of non-linear models

Application of a non-linear regression problem in R

Important considerations in regression modeling

Which covariates to consider in the model?

Dummy variables? What are they?

Orthogonalization effect in R

Chapter 8: Executing A/B Testing and Controlled Experiments

Designing and conducting A/B tests

Concepts

Planning your A/B test

Implementation details

Controlled experiments and causal inference

Enhancing causal inference

Beyond A/B testing - multi-armed bandit tests and factorial designs

Ethical considerations

Common pitfalls and challenges

Strategies for dealing with incomplete data

Mitigating spill-over effects

Adaptive experimentation - when and how to adjust your experiment

Implementing A/B test analysis in R

Step 1 - Generating synthetic data

Step 2 - Exploratory data analysis (EDA)

Step 3 - Statistical testing

Step 4 - Multivariate analysis

Step 5 - Interpreting results

Step 6 - Checking assumptions of the t-test

Step 7 - Effect-size calculation

Step 8 - Power analysis

Step 9 - Post-hoc analyses

Step 10 - Visualizing interaction effects

Chapter 9: Implementing Doubly Robust Estimation

What is doubly robust estimation?

An overview of DR estimation

Technique behind DR

Comparison with other estimation methods

Implementing doubly robust estimation in R

Preparing data for DR analysis

Implementing basic DR estimators

Calculating weight

Crafting the DR estimator.

Discussing doubly robust methods

Estimating variance

Advanced DR techniques (using the tmle and SuperLearner packages)

Balancing flexibility and reliability with DR estimation

Part 3: Advanced Topics and Cutting-Edge Methods

Chapter 10: Analyzing Instrumental Variables

Introduction to instrumental variables

The concept of instrumental variables

The importance of instrumental variables in causal inference

Criteria for instrumental variables

Relevance of the instrumental variable

Exogeneity of the instrumental variable

Exclusion restriction

Strategies for identifying valid instrumental variables

Relevance condition

Exogeneity condition

Demonstrating instrumental variable analysis in R

Using gmm for generalized method of moments

Diagnostics and tests in instrumental variable analysis

Interpretation of results

Challenges and limitations of instrumental variable analysis

Weak instrumental variables

Measurement errors in instrumental variables

Interpretation of instrumental variable estimates

Chapter 11: Investigating Mediation Analysis

What is mediation analysis?

Definition and overview

The importance of mediation analysis

Identifying mediation effects

Criteria for mediation

Testing for mediation

Mediation analysis in R

Preparing data for mediation analysis

Conducting mediation analysis

Interpretation and further steps

Advanced mediation models

Chapter 12: Exploring Sensitivity Analysis

Introduction to sensitivity analysis

Why do we need sensitivity analysis?

Historical context

Sensitivity analysis for causal inference.

How do we use sensitivity analysis?

Types of sensitivity analysis

Key concepts and measures

Implementing sensitivity analysis in R

Using R for sensitivity analysis

Visualizing our findings

Case study

Practical guidelines for conducting sensitivity analysis

Choosing parameters for sensitivity analysis

Limitations and challenges

Advanced topics in sensitivity analysis

Venturing beyond binary treatment

ML approaches

Future directions

Chapter 13: Scrutinizing Heterogeneity in Causal Inference

What is heterogeneity?

Definition of heterogeneity in causality

Case studies and discussion

Examples (more of them)

Understanding the types of heterogeneity

Pre-treatment heterogeneity

Post-treatment heterogeneity

Contextual heterogeneity

Heterogeneous causal effects deep dive

Interaction terms in regression models

Subgroup analysis

ML techniques

Estimation methods for identifying HCEs

Regression Discontinuity Designs

Instrumental variables

Propensity Score Matching

Case study - Heterogeneity in R

Generating synthetic data

Exploratory data analysis

Matching for causal inference

Estimating the ATE

Tailoring interventions to different groups

Conceptual framework

Case study 1 - Educational interventions and their varied effects on different student demographics

Case study 2 - Public health campaigns and their differential impacts on various population segments

Chapter 14: Harnessing Causal Forests and Machine Learning Methods

Introduction to causal forests for causal inference

Historical development and key researchers

Theoretical foundations of causal forests

Conditions necessary for causal forest applications.

Advantages and limitations.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: Das, Subhajit Causal Inference in R

ISBN:

9781803238166

OCLC:

1477882560