book

Causal Inference in R

Name: Causal Inference in R
Author: Subhajit Das
ISBN: 9781837639021

by Subhajit Das

November 2024

Beginner to intermediate

382 pages

12h 16m

English

Packt Publishing

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Causal Inference in R
Contributors
About the author
About the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1:Foundations of Causal Inference
Chapter 1: Introducing Causal Inference
Defining causal inferenceHistorical perspective on causal inferenceWhy do we need causality?Is it an association or really causation?Deep dive causality in real-life settingsExploring the technical aspects of causalitySimpson’s paradoxDefining variablesSummaryReferences
Chapter 2: Unraveling Confounding and Associations
A deep dive into associationsCausality and a fundamental issueIndividual treatment effectAverage treatment effectThe distinction between confounding and associationsDiscussing the concept of bias in causalityAssumptions in causal inferenceStrategies to address confoundingRegression adjustmentPropensity score methodsSummaryReferences
Chapter 3: Initiating R with a Basic Causal Inference Example
Technical requirementsWhat is R? Why use R for causal inference?Getting started with RSetting up the R environmentNavigating the RStudio interfaceBasic R programming conceptsData types in RAdvanced data structuresPackages in RPreparing for causal inference in RPreparing and loading dataExploratory data analysis (EDA)Simple causal inference techniquesComparing means (t-tests)Regression analysisPropensity score matchingCase study – a basic causal analysis in RData preparation and inspectionUnderstanding the dataPerforming causal analysisSummaryReferences
Part 2: Practical Applications and Core Methods
Chapter 4: Constructing Causality Models with Graphs
Technical requirementsBasics of graph theoryTypes of graphs – directed versus undirectedOther graph typologiesWhy we need DAGs in causal scienceGraph representations of variablesMathematical interpretationRepresenting graphs in RBayesian networksConditional independenceExploring Graphical Causal ModelsComparison with Bayesian networksAssumptions in GCMsCase study example of a graph model in RProblem to solve using graphsImplementing in RInterpreting resultsSummaryReferences

Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs
Technical requirementsUnderstanding the flow in GraphsChains and forksCollidersAdjusting for confounding in graphsD-separationDo-operatorThe back door adjustmentThe front door adjustmentPractical R example – back door versus front doorSynthetic dataBack door adjustment in RFront door adjustment in RSummary
Chapter 6: Employing Propensity Score Techniques
Technical requirementsIntroduction to propensity scoresA deep dive into these scoresBalancing confounding variablesCheck for confounding using propensity scoresChallenges and caveatsStratification and subsamplingTheoryApplication of propensity scores in RUnderstanding Propensity Score MatchingConsiderations and limitationsPractical application of PSM in RBalancing methodsSensitivity analysisVisualizing the resultsWeighting in PSM using RSummaryReferences
Chapter 7: Employing Regression Approaches for Causal Inference
Technical requirementsRole of regression in causalityChoosing the appropriate regression modelUnderstanding the nature of the outcome variableConsideration of confounding and interaction effectsModel complexity, parsimony, and assumptionsLinear regression for causal inferenceThe theoryApplication of regression modeling in RSingle versus multivariate regressionTreatment orthogonalizationExample of the FWL theoremModel diagnostics and assumptionsNon-linear regression for causal inferenceOther types of non-linear modelsApplication of a non-linear regression problem in RImportant considerations in regression modelingWhich covariates to consider in the model?Dummy variables? What are they?Orthogonalization effect in RSummaryReferences
Chapter 8: Executing A/B Testing and Controlled Experiments
Technical requirementsDesigning and conducting A/B testsConceptsPlanning your A/B testImplementation detailsControlled experiments and causal inferenceEnhancing causal inferenceBeyond A/B testing – multi-armed bandit tests and factorial designsEthical considerationsCommon pitfalls and challengesStrategies for dealing with incomplete dataMitigating spill-over effectsAdaptive experimentation – when and how to adjust your experimentImplementing A/B test analysis in RStep 1 – Generating synthetic dataStep 2 – Exploratory data analysis (EDA)Step 3 – Statistical testingStep 4 – Multivariate analysisStep 5 – Interpreting resultsStep 6 – Checking assumptions of the t-testStep 7 – Effect-size calculationStep 8 – Power analysisStep 9 – Post-hoc analysesStep 10 – Visualizing interaction effectsSummary
Chapter 9: Implementing Doubly Robust Estimation
Technical requirementsWhat is doubly robust estimation?An overview of DR estimationTechnique behind DRComparison with other estimation methodsImplementing doubly robust estimation in RPreparing data for DR analysisImplementing basic DR estimatorsCalculating weightCrafting the DR estimatorDiscussing doubly robust methodsEstimating varianceAdvanced DR techniques (using the tmle and SuperLearner packages)Balancing flexibility and reliability with DR estimationSummaryReferences
Part 3: Advanced Topics and Cutting-Edge Methods
Chapter 10: Analyzing Instrumental Variables
Technical requirementsIntroduction to instrumental variablesThe concept of instrumental variablesThe importance of instrumental variables in causal inferenceCriteria for instrumental variablesRelevance of the instrumental variableExogeneity of the instrumental variableExclusion restrictionStrategies for identifying valid instrumental variablesRelevance conditionExogeneity conditionDemonstrating instrumental variable analysis in RUsing gmm for generalized method of momentsDiagnostics and tests in instrumental variable analysisInterpretation of resultsChallenges and limitations of instrumental variable analysisWeak instrumental variablesMeasurement errors in instrumental variablesInterpretation of instrumental variable estimatesSummaryReferences
Chapter 11: Investigating Mediation Analysis
Technical requirementsWhat is mediation analysis?Definition and overviewThe importance of mediation analysisIdentifying mediation effectsCriteria for mediationTesting for mediationMediation analysis in RSetting up the R environmentPreparing data for mediation analysisConducting mediation analysisInterpretation and further stepsAdvanced mediation modelsSummaryReferences
Chapter 12: Exploring Sensitivity Analysis
Technical requirementsIntroduction to sensitivity analysisWhy do we need sensitivity analysis?Historical contextSensitivity analysis for causal inferenceHow do we use sensitivity analysis?Types of sensitivity analysisKey concepts and measuresImplementing sensitivity analysis in RUsing R for sensitivity analysisVisualizing our findingsCase studyPractical guidelines for conducting sensitivity analysisChoosing parameters for sensitivity analysisLimitations and challengesAdvanced topics in sensitivity analysisVenturing beyond binary treatmentML approachesFuture directionsSummaryReferences
Chapter 13: Scrutinizing Heterogeneity in Causal Inference
Technical requirementsWhat is heterogeneity?Definition of heterogeneity in causalityCase studies and discussionExamples (more of them)Understanding the types of heterogeneityPre-treatment heterogeneityPost-treatment heterogeneityContextual heterogeneityHeterogeneous causal effects deep diveInteraction terms in regression modelsSubgroup analysisML techniquesEstimation methods for identifying HCEsRegression Discontinuity DesignsInstrumental variablesPropensity Score MatchingCase study – Heterogeneity in RGenerating synthetic dataExploratory data analysisMatching for causal inferenceEstimating the ATETailoring interventions to different groupsConceptual frameworkCase study 1 – Educational interventions and their varied effects on different student demographicsCase study 2 – Public health campaigns and their differential impacts on various population segmentsSummaryReferences
Chapter 14: Harnessing Causal Forests and Machine Learning Methods
Technical requirementsIntroduction to causal forests for causal inferenceHistorical development and key researchersTheoretical foundations of causal forestsConditions necessary for causal forest applicationsAdvantages and limitationsUnderstanding the math behind causal forestsDeep-dive into causal forestsScenario – classroom cohortUsing R to understand causal forestsInstalling and loading necessary packagesSimulating dataTraining a causal forestEstimating treatment effectsValidating the modelExtracting leaf indicesMachine learning approaches to heterogeneous causal inferenceImpact of social media using causal forests in RSetting up the environmentData preparation and preprocessingBuilding and tuning causal forest modelsInterpreting results and model validationSummaryReferences
Chapter 15: Implementing Causal Discovery in R
Technical requirementsIntroduction to causal discoveryDefinition and importanceHistorical backgroundTheoretical foundationsMethods for causal discoveryConstraint-based methodsScore-based methodsHybrid methodsFunctional Causal ModelsWhich causal discovery method should we use?Implementing causal discovery with Bayesian networks in RUsing R packagesScenario for our problemCreating the datasetImplementing PC algorithmUsing bnlearn for Bayesian networksMore causal discovery methodsEstimating causal effectsA multi-algorithm comparative approach to causal discovery in RSetting up and generating dataConstraint-based methods in RScore-based methods in RHybrid methods in RVisualizing causal relationshipsInterpretation from codeFuture stepsSummaryReferences
Index
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Content preview from Causal Inference in R

4 Constructing Causality Models with Graphs

In the previous chapters, we really got our hands dirty with some complex stuff – causality, confounding, association, and how they play out in statistics. We didn’t just talk theory; we got practical, diving into sample regression and propensity score matching, mostly using R, to see how these concepts come alive in real research. Now, we’ll head into the world of graphical models that explain causal relationships in a different way.

This chapter mixes graph theory – which is all about dots and lines and how they connect – with our focus on figuring out cause and effect. Our goal? To give you a toolkit that makes sense of causal connections in various situations. We’ll start with graph theory basics ...

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Causal Inference in R

by Subhajit Das

4

Constructing Causality Models with Graphs

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.