Causal analysis: impact evaluation and causal machine learning with applications in R
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2023]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | ix, 320 Seiten Diagramme |
| ISBN: | 9780262545914 |
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Contents 1 Introduction 1 1.1 About Causality and This Book 1.2 Overview of Topics 3 1 2 Causality and No Causality 11 2.1 Potential Outcomes, Causal Effects, and the Stable Unit Treatment Value Assumption 11 2.2 Treatment Selection Bias 14 3 Social Experiments and Linear Regression 19 3.1 Social Experiments 19 3.2 Effect Identification by Linear Regression 22 3.3 Estimation by Linear Regression and Its Properties 26 3.4 Variance Estimation, Inference, and Goodness of Fit 35 3.5 Extensions to Multiple or Continuous Treatments 49 3.6 Including Covariates 58 4 Selection on Observables 65 4.1 Identification under Selection on Observables . 65 4.2 Linear, Series, and Kernel Regression 70 4.3 Covariate Matching 80 4.4 Propensity Score Matching 88 4.5 Inverse Probability Weighting, Empirical Likelihood, and Entropy Balancing 94 4.6 Doubly Robust Methods 99 4.7 Practical Issues: Common Support and Match Quality 4.8 Multivalued or Continuous Treatments and Distributional Effects 113 4.9 Dynamic Treatment Effects 120 102
viii Contents 4.10 Causal Mechanisms (Mediation Analysis) 126 4.11 Outcome Attrition and Posttreatment Sample Selection 5 Causal Machine Learning 137 5.1 Motivation for Machine Learning and Fields of Application 137 5.2 Double Machine Learning and Partialling out with Lasso Regression 138 5.3 A Survey of Further Machine Learning Algorithms 5.4 Effect Heterogeneity 151 5.5 Optimal Policy Learning 159 5.6 Reinforcement Learning 163 133 145 6 Instrumental Variables 169 6.1 Evaluation of the Local Average Treatment Effect 169 6.2 Instrumental Variable Methods with Covariates 177 6.3 Nonbinary Instruments and Treatments 184 6.4 Sample Selection, Dynamic and Multiple Treatments, and Causal Mechanisms 189 7 Difference-in-Differences 195 7.1 Difference-in-Differences without Covariates 195 7.2 Difference-in-Differences with Covariates 203 7.3 Multiple Periods of Treatment Introduction 207 7.4 Changes-in-Changes 213 8 Synthetic Controls 219 8.1 Estimation and Inference with a Single Treated Unit 219 8.2 Alternative Estimators and Multiple Treated Units 225 9 Regression Discontinuity, Kink, and Bunching Designs 231 9.1 Sharp and Fuzzy Regression Discontinuity Designs 231 9.2 Sharp and Fuzzy Regression Kink Designs 243 9.3 Bunching Designs 248 10 Partial Identification and Sensitivity Analysis 10.1 Partial Identification 255 10.2 Sensitivity Analysis 264 255
Contents 11 12 Treatment Evaluation under Interference Effects 271 11.1 Failure of the Stable Unit Treatment Value Assumption 11.2 Partial Interference 272 11.3 Interference Based on Exposure Mappings 281 Conclusion References 287 Index 311 285 271 |
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| isbn | 9780262545914 |
| language | English |
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| spelling | Huber, Martin 1980- Verfasser (DE-588)139456627 aut Causal analysis impact evaluation and causal machine learning with applications in R Martin Huber Cambridge, Massachusetts ; London, England The MIT Press [2023] © 2023 ix, 320 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Kausalanalyse (DE-588)4163511-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Kausalanalyse (DE-588)4163511-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe, EPUB 978-0-262-37492-7 Erscheint auch als Online-Ausgabe, PDF 978-0-262-37491-0 DE-601 pdf/application http://www.gbv.de/dms/bowker/toc/9780262545914.pdf 2024-02-28 Aggregator Inhaltsverzeichnis Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034347530&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Huber, Martin 1980- Causal analysis impact evaluation and causal machine learning with applications in R Kausalanalyse (DE-588)4163511-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4163511-5 (DE-588)4193754-5 (DE-588)4123623-3 |
| title | Causal analysis impact evaluation and causal machine learning with applications in R |
| title_auth | Causal analysis impact evaluation and causal machine learning with applications in R |
| title_exact_search | Causal analysis impact evaluation and causal machine learning with applications in R |
| title_exact_search_txtP | Causal analysis impact evaluation and causal machine learning with applications in R |
| title_full | Causal analysis impact evaluation and causal machine learning with applications in R Martin Huber |
| title_fullStr | Causal analysis impact evaluation and causal machine learning with applications in R Martin Huber |
| title_full_unstemmed | Causal analysis impact evaluation and causal machine learning with applications in R Martin Huber |
| title_short | Causal analysis |
| title_sort | causal analysis impact evaluation and causal machine learning with applications in r |
| title_sub | impact evaluation and causal machine learning with applications in R |
| topic | Kausalanalyse (DE-588)4163511-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Kausalanalyse Maschinelles Lernen Lehrbuch |
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