Causal Inference in R Decipher Complex Relationships with Advanced R Techniques for Data-Driven Decision-making
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
2024.
|
| Edición: | 1st ed |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009872233406719 |
Tabla de Contenidos:
- 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.
