Regression-Discontinuity Design - Institute for Policy Research

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What to do? • Conduct graphical analyses • Examine residuals • Check to see whether treatment effects are robust to alternative specifications of functional <strong>for</strong>m • Check to see whether treatment effects are robust to alternative pseudo‐cutoffs • Estimate treatment effects using non‐parametric and semi‐parametric approaches
Shadish et al. (under review) Vocabulary RDD mean (s.e.) (1) Linear regression 5.05 (0.57)*** (2) Quadratic regression* 5.91 (0.77)*** (3) Cubic regression 6.04 (1.07)*** (4) Generalized additive Model (GAM) 5.76 (0.72)*** (5) Local linear regression 6.28 (1.12)*** *The quadratic function was selected as the optimal RDD model
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Regression‐Discontinuity Design D
Page 3 and 4:
Design Overview Design Examples Vis
Page 5 and 6:
Real Examples of RD Reading First b
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RDD Visual Depiction Comparison Tre
Page 9 and 10: Two Rationales for RDD 1. Selection
Page 11 and 12: Two Rationales for RDD 1. Selection
Page 13 and 14: 1. Overrides to the cutoff: Sharp,
Page 15 and 16: Fuzzy RD Designs • Requires a dis
Page 17 and 18: Requirements for Estimating Treatme
Page 19 and 20: Implementation Threats to RD Design
Page 21 and 22: Example: AYP Data from Texas Densit
Page 23 and 24: What to do? • This is a big probl
Page 25 and 26: Analytic Threats to RD Design: Miss
Page 27 and 28: Nonlinearities in Functional Form
Page 29 and 30: Here we see a discontinuity between
Page 31 and 32: Functional Form: Interactions • S
Page 33 and 34: If we superimpose the regression li
Page 35 and 36: Here we see an example where the tr
Page 37 and 38: Adding Nonlinear Terms to the Model
Page 39 and 40: Adding Nonlinear and Interaction Te
Page 41 and 42: Adding Terms, Continued • When in
Page 43 and 44: Example of Parametric Plot used aga
Page 45 and 46: Non‐parametric Approach lim x↓c
Page 47 and 48: Non‐parametric plot Local linear
Page 49 and 50: Semi‐Parametric Plot RD data from
Page 51 and 52: What to do? • Check to see whethe
Page 53 and 54: Shadish, Galindo, Wong, V., Steiner
Page 55 and 56: RD Design assumption 2 • Continui
Page 57 and 58: Implementation assumption 2 • No
Page 59: Analytic assumption 1 continued •
Page 63 and 64: RDD Example 2
Page 65 and 66: Best case scenario - regression lin
Page 67 and 68: Correct specification of functional
Page 69 and 70: What to do (2) • Parametric appro
Page 71 and 72: What to do (4) • State‐of‐art
Page 73 and 74: What to do (5) • Move to a tie br
Page 75 and 76: Example Cocaine Project • Include
Page 77 and 78: What to do (7) • Estimation throu
Page 79 and 80: 2. Dealing with Overrides to the cu
Page 81 and 82: 2. Overrides to the cutoff • Mult
Page 83 and 84: Specification of Functional Form Pa
Page 85 and 86: Specification of Functional Form No
Page 87 and 88: Overrides to cutoff (2) Alternative
Page 89 and 90: Final estimates Function al form Pa
Page 91 and 92: Addressing Potential Issues with us
Page 93 and 94: IS Wong et al. a Fallible RD or Ins
Page 95 and 96: African‐American Home language Sp
Page 97 and 98: Fallible RD? • Now we look for di
Page 99 and 100: McCrary Test for New Jersey
Page 101 and 102: Summarizing Checks - Conditional Me
Page 103 and 104: Other Kinds of Threats with RD 1. L
Page 105 and 106: Variance of Impact Estimator in RA
Page 107 and 108: Variance of Impact Estimator in RDD
Page 109 and 110: Power Considerations Unique to RDD
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Generalizing Beyond The Cut‐off I
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Observed vs Counterfactual Outcomes
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Questions About Generalizing From R
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What Do We Mean By Comparison Group
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Our Purpose • Use experimental da
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What Is The Target Parameter? • A
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What About The Above The Cut‐Off
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Root Mean Square Error In Experimen
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Post-Treatment Medicaid Expenditure
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Results For Number of Unpaid Worker
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Results For Expenditures With Expen
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Conclusions re Using a Comparison R
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Implications for Practice • Which
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RD with Multiple Cutoffs Design (as
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Analyzing RDs with Multiple Cutoffs
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Examples • Black, Galdo, and Smit
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Issue 2: To pool or not pool across
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Pooling is Already Common in Social
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What is Special about Pooling RD Es
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Estimating Treatment Effects across
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Estimating Treatment Effects across
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Generalizing Across Multiple Assign
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Multivariate RDD with Two Assignmen
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Recent Education Examples of RDDs w
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Frontier‐specific Effect (τ R )
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Requirements for a valid Multivaria
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Frontier Approach Estimates the dis
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Univariate Approach Addresses dimen
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Monte Carlo Study Wong, V., Steiner
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Implications for Practice • Which
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Regression-Discontinuity Design - Institute for Policy Research

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