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

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
	Summary
	References
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
	Summary
	References
Part 2: Practical Applications and Core Methods
Chapter 4: Constructing Causality Models with Graphs
	Technical requirements
	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
	Summary
	References
Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs
	Technical requirements
	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
	Summary
Chapter 6: Employing Propensity Score Techniques
	Technical requirements
	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
	Summary
	References
Chapter 7: Employing Regression Approaches for Causal Inference
	Technical requirements
	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
	Summary
	References
Chapter 8: Executing A/B Testing and Controlled Experiments
	Technical requirements
	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
	Summary
Chapter 9: Implementing Doubly Robust Estimation
	Technical requirements
	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
	Summary
	References
Part 3: Advanced Topics and Cutting-Edge Methods
Chapter 10: Analyzing Instrumental Variables
	Technical requirements
	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
	Summary
	References
Chapter 11: Investigating Mediation Analysis
	Technical requirements
	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
		Setting up the R environment
		Preparing data for mediation analysis
		Conducting mediation analysis
		Interpretation and further steps
		Advanced mediation models
	Summary
	References
Chapter 12: Exploring Sensitivity Analysis
	Technical requirements
	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
	Summary
	References
Chapter 13: Scrutinizing Heterogeneity in Causal Inference
	Technical requirements
	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
	Summary
	References
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
		Conditions necessary for causal forest applications
		Advantages and limitations
	Understanding the math behind causal forests
		Deep-dive into causal forests
		Scenario – classroom cohort
	Using R to understand causal forests
		Installing and loading necessary packages
		Simulating data
		Training a causal forest
		Estimating treatment effects
		Validating the model
		Extracting leaf indices
	Machine learning approaches to heterogeneous causal inference
	Impact of social media using causal forests in R
		Setting up the environment
		Data preparation and preprocessing
		Building and tuning causal forest models
		Interpreting results and model validation
	Summary
	References
Chapter 15: Implementing Causal Discovery in R
	Technical requirements
	Introduction to causal discovery
		Definition and importance
		Historical background
		Theoretical foundations
	Methods for causal discovery
		Constraint-based methods
		Score-based methods
		Hybrid methods
		Functional Causal Models
		Which causal discovery method should we use?
	Implementing causal discovery with Bayesian networks in R
		Using R packages
		Scenario for our problem
		Creating the dataset
		Implementing PC algorithm
		Using bnlearn for Bayesian networks
		More causal discovery methods
		Estimating causal effects
	A multi-algorithm comparative approach to causal discovery in R
		Setting up and generating data
		Constraint-based methods in R
		Score-based methods in R
		Hybrid methods in R
		Visualizing causal relationships
		Interpretation from code
		Future steps
	Summary
	References
Index
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