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Home > Data > Data Science > Causal Inference in R
Causal Inference in R
Causal Inference in R

Causal Inference in R: Decipher complex relationships with advanced R techniques for data-driven decision-making

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Causal Inference in R

Introducing Causal Inference

In this inaugural chapter, let’s explore the topic of causal inference a bit. For some, this may be a new topic; for others, it might be somewhat familiar. However, whether you find this topic intimidating or not depends less on your existing statistical knowledge and more on your interest in the subject and your consistent effort throughout the book.

Our exploration begins with three pivotal questions: What exactly is causal inference? Why is it indispensable? How can it be effectively utilized? To clarify these concepts, we’ll use both fictitious and real-life scenarios.

Approach this chapter with unhindered curiosity and an open mind. Be prepared to encounter concepts and terminology that might initially seem abstruse. Don’t worry, though—we will be with you every step of the way, ensuring you understand everything clearly and thoroughly as we explore causal inference together.

In this chapter, we will cover the following...

Defining causal inference

Picture yourself as a teacher contemplating a curious phenomenon among high school students: the relationship between sleeping late and catching the school bus. An initial hypothesis might be, “Sleeping late causes students to miss their school bus.” This stems from a personal experience: I slept late and consequently missed the bus. However, this hypothesis might be challenged upon observing graduate students, who, despite sleeping late, consistently catch their buses.

This scenario exemplifies the complex, often misleading nature of causality. The observed association—sleeping late and missing buses—doesn’t inherently imply causation. Here, various other factors could be at play. Perhaps high school students have earlier bus schedules, or graduate students, despite sleeping late, can wake up early and not miss their transportation. It’s plausible that the initial observation of sleeping late and causing one to...

Historical perspective on causal inference

In Hellenic philosophy, ancient Greece played a pivotal role. Pre-Socratic philosophers such as Thales and Heraclitus explored the nature of change and causality. Ancient Greek philosophers contributed significantly to systematic approaches to understanding causality by examining cause-and-effect relationships. They introduced important causal concepts, including the idea that nothing comes from nothing (attributed to Parmenides). While not originating the principle of sufficient reason, their work laid the foundations for later philosophical developments. Their ideas have influenced our understanding of causation, though modern concepts have evolved significantly, incorporating insights from various traditions, scientific advancements, and mathematical frameworks.

Aristotle, however, provided a more structured approach to causality with his four causes theory:

  • Material cause: The material from which something is made (e.g., the...

Why do we need causality?

Beyond understanding the theoretical and historical underpinnings, one must ponder the practical necessity of causality first. We shall discuss examples of the ubiquitous application of causality across various industries. For instance, enterprises leverage causal inference techniques to gain deeper insights into customer behaviors, needs, and preferences. They employ these methods to elucidate both natural and anthropogenic phenomena. Mastery of causal inference equips you with an extremely powerful tool, rendering you an invaluable asset in any team or organizational context. Your proficiency in this domain can significantly contribute to the overarching objective of delivering value to stakeholders.

Let's discuss further why causality is not only an intellectually rewarding area but also practically indispensable.

In medical and public health arenas, causal inference is vital for assessing treatment efficacy. Randomized controlled trials (RCTs...

Is it an association or really causation?

It’s tempting to attribute causality to superficial observations, mistaking mere associations for causation. Take, for instance, the observation that social media posts made later in the day receive fewer likes and comments, suggesting reduced engagement. One might hastily conclude that the timing of these posts is the causal factor. However, without rigorous statistical testing, such claims remain speculative. In this book, we will teach you how to conduct these necessary tests, distinguishing between simple association and true causation.

In statistics, we discuss association, causation, and correlation. While correlation is often used interchangeably with association in everyday conversations, they have distinct meanings in statistical contexts. So, what is the difference between association and correlation?

In causality, association encapsulates a general linkage between two variables, without explicitly characterizing the...

Deep dive causality in real-life settings

Let's walk through a study titled Inked into Crime? An Examination of the Causal Relationship between Tattoos and Life-Course Offending among Males from the Cambridge Study in Delinquent Development [1]. It’s about a study to ascertain whether or not there exists a causal connection between the presence of tattoos and the propensity for criminal behavior across one’s lifespan. Analyzing data from 411 British males, the researchers utilized propensity score matching—a statistical technique frequently used to ascertain causal inference (which we shall learn about later in this book, in Chapter 6). This approach meticulously dissects the complexity between the ink on skin and the propensity for crime, offering a more refined perspective on this age-old debate.

Rooted in the shadow of 19th-century criminological thought, specifically the theories of Lombroso, tattoos have long been cast in the dim light of criminality...

Exploring the technical aspects of causality

From the previous section, it is evident that causal inference involves employing observational or experimental data to establish causal links, utilizing various statistical methods and theories to measure the influence of one variable (the “treatment” or “intervention”) on another (the “outcome” or the “effect”).

From a statistical vantage point, it focuses on estimating the counterfactual, hypothesizing the outcomes in alternate scenarios where the treatment was absent. This necessitates assumptions about data and underlying mechanisms, including the exclusion of unmeasured confounders. Let’s go over these concepts one by one.

Counterfactual analysis

Counterfactual analysis involves exploring "what-if" scenarios to understand the effects of actions that didn't occur. It is used to estimate the causal impact of interventions by imagining alternative outcomes...

Summary

This chapter introduced the concept of causality and its importance across various fields. A brief historical overview acknowledged the contributions of ancient philosophers and modern statisticians in developing causal inference methods. We started by defining causal inference and distinguishing it from association and correlation with practical examples. We also touched on complex ideas such as potential outcomes, confounding variables, and Simpson’s paradox, explaining how they affect causal studies.

Finally, the chapter underscored the importance of causal inference in making informed decisions in our data-driven world. This foundation prepares you for a deeper exploration of causal inference in subsequent chapters.

References

  1. Inked into Crime: https://www.sciencedirect.com/science/article/abs/pii/S0047235213001189.
  2. Causal or Spurious: Using Propensity Score Matching to Detangle the Relationship between Violent Video Games and Violent Behavior: https://www.researchgate.net/publication/257252863_Causal_or_Spurious_Using_Propensity_Score_Matching_to_Detangle_the_Relationship_between_Violent_Video_Games_and_Violent_Behavior.
  3. Imbens, G.W., Rubin, D.B. (2010). Rubin Causal Model. In: Durlauf, S.N., Blume, L.E. (eds) Microeconometrics. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280816_28.
  4. Mill’s methods, https://beisecker.faculty.unlv.edu/Courses/Phi-102/Mills_Methods.htm.
  5. Peter Armitage, Fisher, Bradford Hill, and randomization, International Journal of Epidemiology, Volume 32, Issue 6, December 2003, Pages 925–928, https://doi.org/10.1093/ije/dyg286.
  6. BIOS 6611 (2021, July 31) , Neyman-Pearson Approach to...
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Key benefits

  • Explore causal analysis with hands-on R tutorials and real-world examples
  • Grasp complex statistical methods by taking a detailed, easy-to-follow approach
  • Equip yourself with actionable insights and strategies for making data-driven decisions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.

What you will learn

  • Get a solid understanding of the fundamental concepts and applications of causal inference
  • Utilize R to construct and interpret causal models
  • Apply techniques for robust causal analysis in real-world data
  • Implement advanced causal inference methods, such as instrumental variables and propensity score matching
  • Develop the ability to apply graphical models for causal analysis
  • Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis
  • Become proficient in the practical application of doubly robust estimation using R
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Table of Contents

20 Chapters
Part 1:Foundations of Causal Inference Chevron down icon Chevron up icon
Part 1:Foundations of Causal Inference
Chapter 1: Introducing Causal Inference Chevron down icon Chevron up icon
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
Summary
References
Chapter 2: Unraveling Confounding and Associations Chevron down icon Chevron up icon
Chapter 2: Unraveling Confounding and Associations
A deep dive into associations
Causality and a fundamental issue
The distinction between confounding and associations
Discussing the concept of bias in causality
Assumptions in causal inference
Strategies to address confounding
Summary
References
Chapter 3: Initiating R with a Basic Causal Inference Example Chevron down icon Chevron up icon
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
Basic R programming concepts
Preparing for causal inference in R
Simple causal inference techniques
Case study – a basic causal analysis in R
Summary
References
Part 2: Practical Applications and Core Methods Chevron down icon Chevron up icon
Part 2: Practical Applications and Core Methods
Chapter 4: Constructing Causality Models with Graphs Chevron down icon Chevron up icon
Chapter 4: Constructing Causality Models with Graphs
Technical requirements
Basics of graph theory
Graph representations of variables
Exploring Graphical Causal Models
Case study example of a graph model in R
Summary
References
Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs Chevron down icon Chevron up icon
Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs
Technical requirements
Understanding the flow in Graphs
Adjusting for confounding in graphs
Practical R example – back door versus front door
Summary
Chapter 6: Employing Propensity Score Techniques Chevron down icon Chevron up icon
Chapter 6: Employing Propensity Score Techniques
Technical requirements
Introduction to propensity scores
Stratification and subsampling
Understanding Propensity Score Matching
Weighting in PSM using R
Summary
References
Chapter 7: Employing Regression Approaches for Causal Inference Chevron down icon Chevron up icon
Chapter 7: Employing Regression Approaches for Causal Inference
Technical requirements
Role of regression in causality
Choosing the appropriate regression model
Linear regression for causal inference
Model diagnostics and assumptions
Non-linear regression for causal inference
Important considerations in regression modeling
Summary
References
Chapter 8: Executing A/B Testing and Controlled Experiments Chevron down icon Chevron up icon
Chapter 8: Executing A/B Testing and Controlled Experiments
Technical requirements
Designing and conducting A/B tests
Controlled experiments and causal inference
Common pitfalls and challenges
Implementing A/B test analysis in R
Summary
Chapter 9: Implementing Doubly Robust Estimation Chevron down icon Chevron up icon
Chapter 9: Implementing Doubly Robust Estimation
Technical requirements
What is doubly robust estimation?
Implementing doubly robust estimation in R
Discussing doubly robust methods
Summary
References
Part 3: Advanced Topics and Cutting-Edge Methods Chevron down icon Chevron up icon
Part 3: Advanced Topics and Cutting-Edge Methods
Chapter 10: Analyzing Instrumental Variables Chevron down icon Chevron up icon
Chapter 10: Analyzing Instrumental Variables
Technical requirements
Introduction to instrumental variables
Criteria for instrumental variables
Strategies for identifying valid instrumental variables
Demonstrating instrumental variable analysis in R
Challenges and limitations of instrumental variable analysis
Summary
References
Chapter 11: Investigating Mediation Analysis Chevron down icon Chevron up icon
Chapter 11: Investigating Mediation Analysis
Technical requirements
What is mediation analysis?
Identifying mediation effects
Mediation analysis in R
Summary
References
Chapter 12: Exploring Sensitivity Analysis Chevron down icon Chevron up icon
Chapter 12: Exploring Sensitivity Analysis
Technical requirements
Introduction to sensitivity analysis
Sensitivity analysis for causal inference
Implementing sensitivity analysis in R
Practical guidelines for conducting sensitivity analysis
Advanced topics in sensitivity analysis
Summary
References
Chapter 13: Scrutinizing Heterogeneity in Causal Inference Chevron down icon Chevron up icon
Chapter 13: Scrutinizing Heterogeneity in Causal Inference
Technical requirements
What is heterogeneity?
Understanding the types of heterogeneity
Heterogeneous causal effects deep dive
Estimation methods for identifying HCEs
Case study – Heterogeneity in R
Tailoring interventions to different groups
Summary
References
Chapter 14: Harnessing Causal Forests and Machine Learning Methods Chevron down icon Chevron up icon
Chapter 14: Harnessing Causal Forests and Machine Learning Methods
Technical requirements
Introduction to causal forests for causal inference
Historical development and key researchers
Theoretical foundations of causal forests
Understanding the math behind causal forests
Using R to understand causal forests
Machine learning approaches to heterogeneous causal inference
Impact of social media using causal forests in R
Summary
References
Chapter 15: Implementing Causal Discovery in R Chevron down icon Chevron up icon
Chapter 15: Implementing Causal Discovery in R
Technical requirements
Introduction to causal discovery
Methods for causal discovery
Implementing causal discovery with Bayesian networks in R
A multi-algorithm comparative approach to causal discovery in R
Summary
References
Index Chevron down icon Chevron up icon
Index
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About the author

Profile icon Subhajit Das
Subhajit Das
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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.
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We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela