Introduction

Kapitel in diesem Buch

1. Frontmatter i
2. Contents vii
3. Acknowledgments ix
4. Introduction
5. Introductory Note 1
6. What Is Causal Inference? 3
7. Do Not Confuse Correlation with Causality 6
8. OptimizationMakes Everything Endogenous 8
9. Example: Identifying Price Elasticity of Demand 10
10. Conclusion 14
11. Probability and Regression Review 16
12. Directed Acyclic Graphs
13. Introduction 96
14. Introduction to DAG Notation 97
15. Potential Outcomes Causal Model
16. Introduction 119
17. Physical Randomization 123
18. Randomization Inference 148
19. Conclusion 174
20. Matching and Subclassification
21. Subclassification 175
22. Exact Matching 191
23. Approximate Matching 198
24. Regression Discontinuity
25. Huge Popularity of Regression Discontinuity 241
26. Estimation Using an RDD 252
27. Challenges to Identification 282
28. Replicating a Popular Design: The Close Election 289
29. Regression Kink Design 312
30. Conclusion 313
31. Instrumental Variables
32. History of Instrumental Variables: Father and Son 315
33. Intuition of Instrumental Variables 319
34. Homogeneous Treatment Effects 323
35. Parental Methamphetamine Abuse and Foster Care 329
36. The Problem of Weak Instruments 337
37. Heterogeneous Treatment Effects 346
38. Applications 352
39. Popular IV Designs 359
40. Conclusion 384
41. Panel Data
42. DAG Example 386
43. Estimation 388
44. Data Exercise: Survey of Adult Service Providers 396
45. Conclusion 405
46. Difference-in-Differences
47. John Snow’s Cholera Hypothesis 406
48. Estimation 411
49. Inference 423
50. Providing Evidence for Parallel Trends Through Event Studies and Parallel Leads 425
51. The Importance of Placebos in DD 433
52. Twoway Fixed Effects with Differential Timing 461
53. Conclusion 509
54. Synthetic Control
55. Introducing the Comparative Case Study 511
56. Prison Construction and Black Male Incarceration 525
57. Conclusion 540
58. Bibliography 541
59. Permissions 555
60. Index 561