Statistical approaches to causal analysis:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
Sage
[2021]
|
| Series: | The Sage quantitative research kit
10th volume |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Enthält Literaturverzeichnis Seite 217-228 und Index |
| Physical Description: | xxix, 234 Seiten Illustrationen, Diagramme |
| ISBN: | 9781526424730 |
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Record in the Search Index
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| adam_text | CONTENTS List of Figures and Tables About the Author Acknowledgement Preface 1 2 Introduction xiii xxiii xxv xxvii 1 Internal Validity External Validity Threats to Validity Randomisation Non-Experimental Research A Pragmatic Definition of Causation Prediction Versus Explanation Causal Inference Requires External Information Estimation Versus Hypothesis Testing Prerequisites Notation The R statistical programming environment Installing and Using R and RStudio R Packages Structure of This Book 2 2 3 6 7 9 10 11 13 13 14 14 16 16 17 Conditioning 19 Simulated Data Set Bias and Inconsistency Obtaining a Biased Estimate of the Causal Effect Covariate Adjustment Visualising Covariate Adjustment Covariate Adjustment Depends on Strong Assumptions 21 21 25 26 27 30
vii! 3 STATISTICAL APPROACHES TO CAUSAL ANALYSIS Sample Selection The Bias-Variance Trade-Off Subclassification Matching Weighting Computing the Weights The Problem of Measurement Classical Test Theory Model for Measurement Error Reliability Discussion The Curse of Dimensionality 30 32 33 35 39 39 42 43 44 46 47 Directed Acyclic Graphs 51 DAGs Are Not Path Models DAG Terminology and VariableRoles Exposure Outcome Mediator Confounder Proxy Confounder Instrument Competing Exposure Collider ¿-Separation, ¿-Connectedness and Statistical Independence Conditioning Conditioning on Colliders Colliders and the Real World Spurious Paths Unobservables Conditioning on Mediators Criteria for Valid Causal Inference Back-Door Criterion Front-Door Criterion Minimal and Sufficient Adjustment Sets Simultaneous Estimation of Causal Effects Measurement Error and DAGs Using DAGitty Practical Recommendations 53 53 54 55 55 55 56 56 57 58 58 59 61 62 64 69 69 72 72 73 74 75 76 77 81 *
CONTENTS 4 5 Rubin s Causal Model and the Propensity Score ix 85 The Counterfactual Framework Defining Causal Effects Under Rubin s Causal Model The Fundamental Problem of Causal Inference Ignorability Bias When Ignorability Does Not Exist Baseline Bias Differential Treatment Effect Bias Conditional Ignorability Conditional Treatment Effects Example: Estimating ATT, ATU and ATE via Linear Regression The Propensity Score Approximating an Experiment Simulated Data Set Propensity Scores Estimating Propensity Scores via Logistic Regression Solving the Curse of Dimensionality Propensity Score Estimation via Boosted Classification Trees Comparing the Two Sets of Propensity Scores Assumptions of Propensity Score Methods Ignorability Stable Unit Treatment Value Assumption Positivity 86 87 88 90 92 92 93 93 94 95 97 98 101 101 102 108 108 115 116 116 116 117 Propensity Score Analysis 119 Simulated Data Set Descriptive Statistics and Biased Treatment Effect Estimate Obtaining a Biased Estimate of the Treatment Effect Propensity Score Matching Matching Algorithms Estimating Treatment Effects with Matching Example Analysis Stratifying on the Propensity Score Weighting with the propensity score From Propensity Scores to Weights Stabilised Weights and Truncated Weights Example of an Analysis Using Propensity Score Weights Doubly Robust Estimation 120 121 121 124 124 129 .129 131 136 139 141 143 147
x 6 STATISTICAL APPROACHES TO CAUSAL ANALYSIS Instrumental Variable Analysis 151 Endogeneity and Bias Defining Instrumental Variables Finding an Instrument The Two-Stage Least Squares Estimator Simultaneous Two-Stage Least Squares Sample Size Issues Measurement Error Imperfect Measurement of the Exposure Measurement Error in the Instrument Local Average Treatment Effects Assumptions of Instrumental Variable Analysis 153 156 159 162 163 164 165 169 169 170 172 174 175 Regression Discontinuity Design 177 The Forcing Variable and Treatment Assignment Sharp RDD 179 180 182 191 194 197 199 202 Step 1: Obtain the Predicted Values of the Exposure Variable Step 2: Estimate the Treatment Effect 7 Extrapolation via Parametric Regression Example of Sharp RDD Analysis Fuzzy RDD Example ofFuzzy RDD Analysis Local Average Treatment Effects Assumptions of the RDD 8 « Conclusion 205 Some Practical Advice Disclose Your DAG Test Your DAG Sensitivity Analysis Don t Ignore Precision Don t Fool Yourself What to Learn Next Campbell and Stanley s Versus Rubin s Perspectives on Causation More About DAGs 207 207 208 208 209 209 210 210 210
CONTENTS Principal Stratification Fixed Effects Closing Glossary References Index xi 210 211 211 213 217 229
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| adam_txt |
CONTENTS List of Figures and Tables About the Author Acknowledgement Preface 1 2 Introduction xiii xxiii xxv xxvii 1 Internal Validity External Validity Threats to Validity Randomisation Non-Experimental Research A Pragmatic Definition of Causation Prediction Versus Explanation Causal Inference Requires External Information Estimation Versus Hypothesis Testing Prerequisites Notation The R statistical programming environment Installing and Using R and RStudio R Packages Structure of This Book 2 2 3 6 7 9 10 11 13 13 14 14 16 16 17 Conditioning 19 Simulated Data Set Bias and Inconsistency Obtaining a Biased Estimate of the Causal Effect Covariate Adjustment Visualising Covariate Adjustment Covariate Adjustment Depends on Strong Assumptions 21 21 25 26 27 30
vii! 3 STATISTICAL APPROACHES TO CAUSAL ANALYSIS Sample Selection The Bias-Variance Trade-Off Subclassification Matching Weighting Computing the Weights The Problem of Measurement Classical Test Theory Model for Measurement Error Reliability Discussion The 'Curse of Dimensionality' 30 32 33 35 39 39 42 43 44 46 47 Directed Acyclic Graphs 51 DAGs Are Not Path Models DAG Terminology and VariableRoles Exposure Outcome Mediator Confounder Proxy Confounder Instrument Competing Exposure Collider ¿-Separation, ¿-Connectedness and Statistical Independence Conditioning Conditioning on Colliders Colliders and the Real World Spurious Paths Unobservables Conditioning on Mediators Criteria for Valid Causal Inference Back-Door Criterion Front-Door Criterion Minimal and Sufficient Adjustment Sets Simultaneous Estimation of Causal Effects Measurement Error and DAGs Using DAGitty Practical Recommendations 53 53 54 55 55 55 56 56 57 58 58 59 61 62 64 69 69 72 72 73 74 75 76 77 81 *
CONTENTS 4 5 Rubin's Causal Model and the Propensity Score ix 85 The Counterfactual Framework Defining Causal Effects Under Rubin's Causal Model The Fundamental Problem of Causal Inference Ignorability Bias When Ignorability Does Not Exist Baseline Bias Differential Treatment Effect Bias Conditional Ignorability Conditional Treatment Effects Example: Estimating ATT, ATU and ATE via Linear Regression The Propensity Score Approximating an Experiment Simulated Data Set Propensity Scores Estimating Propensity Scores via Logistic Regression Solving the Curse of Dimensionality Propensity Score Estimation via Boosted Classification Trees Comparing the Two Sets of Propensity Scores Assumptions of Propensity Score Methods Ignorability Stable Unit Treatment Value Assumption Positivity 86 87 88 90 92 92 93 93 94 95 97 98 101 101 102 108 108 115 116 116 116 117 Propensity Score Analysis 119 Simulated Data Set Descriptive Statistics and Biased Treatment Effect Estimate Obtaining a Biased Estimate of the Treatment Effect Propensity Score Matching Matching Algorithms Estimating Treatment Effects with Matching Example Analysis Stratifying on the Propensity Score Weighting with the propensity score From Propensity Scores to Weights Stabilised Weights and Truncated Weights Example of an Analysis Using Propensity Score Weights Doubly Robust Estimation 120 121 121 124 124 129 .129 131 136 139 141 143 147
x 6 STATISTICAL APPROACHES TO CAUSAL ANALYSIS Instrumental Variable Analysis 151 Endogeneity and Bias Defining Instrumental Variables Finding an Instrument The Two-Stage Least Squares Estimator Simultaneous Two-Stage Least Squares Sample Size Issues Measurement Error Imperfect Measurement of the Exposure Measurement Error in the Instrument Local Average Treatment Effects Assumptions of Instrumental Variable Analysis 153 156 159 162 163 164 165 169 169 170 172 174 175 Regression Discontinuity Design 177 The Forcing Variable and Treatment Assignment Sharp RDD 179 180 182 191 194 197 199 202 Step 1: Obtain the Predicted Values of the Exposure Variable Step 2: Estimate the Treatment Effect 7 Extrapolation via Parametric Regression Example of Sharp RDD Analysis Fuzzy RDD Example ofFuzzy RDD Analysis Local Average Treatment Effects Assumptions of the RDD 8 « Conclusion 205 Some Practical Advice Disclose Your DAG Test Your DAG Sensitivity Analysis Don't Ignore Precision Don't Fool Yourself What to Learn Next Campbell and Stanley's Versus Rubin's Perspectives on Causation More About DAGs 207 207 208 208 209 209 210 210 210
CONTENTS Principal Stratification Fixed Effects Closing Glossary References Index xi 210 211 211 213 217 229 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T18:39:46Z |
| indexdate | 2024-07-10T09:16:08Z |
| institution | BVB |
| isbn | 9781526424730 |
| language | English |
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| physical | xxix, 234 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
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| series | The Sage quantitative research kit |
| series2 | The Sage quantitative research kit |
| spelling | McBee, Matthew Verfasser aut Statistical approaches to causal analysis Matthew McBee Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne Sage [2021] xxix, 234 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier The Sage quantitative research kit 10th volume Enthält Literaturverzeichnis Seite 217-228 und Index Kausalanalyse (DE-588)4163511-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Kausalanalyse (DE-588)4163511-5 s DE-604 The Sage quantitative research kit 10th volume (DE-604)BV047607953 10 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032995595&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McBee, Matthew Statistical approaches to causal analysis The Sage quantitative research kit Kausalanalyse (DE-588)4163511-5 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4163511-5 (DE-588)4056995-0 |
| title | Statistical approaches to causal analysis |
| title_auth | Statistical approaches to causal analysis |
| title_exact_search | Statistical approaches to causal analysis |
| title_exact_search_txtP | Statistical approaches to causal analysis |
| title_full | Statistical approaches to causal analysis Matthew McBee |
| title_fullStr | Statistical approaches to causal analysis Matthew McBee |
| title_full_unstemmed | Statistical approaches to causal analysis Matthew McBee |
| title_short | Statistical approaches to causal analysis |
| title_sort | statistical approaches to causal analysis |
| topic | Kausalanalyse (DE-588)4163511-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Kausalanalyse Statistik |
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