Regression Discontinuity - Institute for Policy Research

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What is Special about Pooling RD Estimates? One Argument • Very little -- just a different kind of causal estimate to pool • Actually, it is a particularly unproblematic case since no observational studies and a strong presumption of bias-free estimates • Hence pooling is only about precision of whatever average estimate you come up with; not really about bias at all.
What is Special about Pooling RD Estimates? A Different Argument • Each site or study with RD has less power than RE and so SE’s larger • RD has less transparent assumptions about bias than RE, and so • Greater uncertainty about bias in estimates and in standard errors compared to RE
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Regression-Discontinuity Design Day
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Design Overview Design Examples Vis
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RDD Visual Depiction Comparison
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RDD Visual Depiction Discontinuity,
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Two Rationales for RDD 1. Selection
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Required Assumptions for RD design
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Sharp RD Designs • Requires that
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Estimating Treatment Effects in Fuz
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“Non”-RD designs • No discont
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2. Manipulation of the assignment v
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Diagnostic test • Can perform “
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Analytic Threats to RD Design: Para
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The size of the discontinuity at th
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Functional Form • Put more techni
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Functional Form: Interactions • S
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If we superimpose the regression li
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Here we see an example where the tr
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Adding Nonlinear Terms to the Model
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Adding Nonlinear and Interaction Te
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Adding Terms, Continued • When in
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Example of Parametric Plot used aga
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Non-parametric Approach lim x¯c E[
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3. Semi-Parametric Approach Y i = t
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Problem of Incorrect Bandwidth Sele
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RDD Example 1
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RD Design assumption 1 • Probabil
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Implementation assumption 2 • No
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Analytic assumption 1 continued •
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Shadish et al. (2011) Vocabulary RD
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RDD Example 2
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Best case scenario - regression lin
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Correct specification of functional
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What to do (2) • Parametric appro
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What to do (4) 0 50 100 150 locpoly
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What to do (5) • Move to a tie br
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Example Cocaine Project • Include
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What to do (7) • Estimation throu
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2. Dealing with Overrides to the cu
Page 79 and 80: 2. Overrides to the cutoff • Mult
Page 81 and 82: Specification of Functional Form Pa
Page 83 and 84: Specification of Functional Form No
Page 85 and 86: Overrides to cutoff (2) Alternative
Page 87 and 88: Final estimates Function al form Pa
Page 89 and 90: Addressing Potential Issues with us
Page 91 and 92: African-American Home language Span
Page 93 and 94: What about cutoff Shift in Number o
Page 95 and 96: McCrary Test for New Jersey
Page 97 and 98: Take Home Message • Like RE, the
Page 99 and 100: Inadequate statistical power • Ne
Page 101 and 102: Power Considerations Common to Rand
Page 103 and 104: What “Uniquely” Affects Power i
Page 105 and 106: Generalizing Beyond The Cut-off In
Page 107 and 108: Adding an Untreated RD Comparison F
Page 109 and 110: Creating A Regression Discontinuity
Page 111 and 112: Pretest and Posttest RD • Imagine
Page 113 and 114: What Is The Target Parameter? • A
Page 115 and 116: Table 1: Bias At the Cutoff Adding
Page 117 and 118: Summary of what the Pretest- Supple
Page 119 and 120: Additional Topic not covered in Cla
Page 121 and 122: Sample Data with Multiple Cutoffs O
Page 123 and 124: Typical estimation strategy for RDs
Page 125 and 126: Issue 1: To standardize or not stan
Page 127 and 128: Generalizing Treatment Effects Acro
Page 129: …Also Common in • Meta-Analysis
Page 133 and 134: Benefits and Tradeoffs of Option 1
Page 135 and 136: Benefits and Tradeoffs of Option 2
Page 137 and 138: Distribution of Units in an RD with
Page 139 and 140: Multivariate RDD with Two Assignmen
Page 141 and 142: The Average Treatment Effect along
Page 143 and 144: Treatment Weights for τ MRD Weight
Page 145 and 146: Estimating Treatment Effects Four P
Page 147 and 148: Centering Approach Procedure allows
Page 149 and 150: IV Approach (1) Rather than exclude
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Regression Discontinuity - Institute for Policy Research

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