Regression-Discontinuity Design - Institute for Policy Research

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30 Observed vs Counterfactual Outcomes in a Hypothetical RD Below The Cut-off (No Treatment) Above The Cut-off (Treatment) 20 Observed Treated Outcomes Outcome 10 Counterfactual Treated Outcomes 0 Counterfactual Untreated Outcomes Observed Untreated Outcomes -10 -10 -5 0 5 10 Assignment Variable
Questions About Generalizing From RD • In any RD study, ask: 1. Is this cut‐off sub‐population relevant to policy or scientific questions? 2. Does this assignment variable define other subpopulations that are more relevant to policy or science? 3. What population actually is of interest? What randomized experiment would I like to run?
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Regression‐Discontinuity Design D
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Design Overview Design Examples Vis
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Real Examples of RD Reading First b
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RDD Visual Depiction Comparison Tre
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Two Rationales for RDD 1. Selection
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Two Rationales for RDD 1. Selection
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1. Overrides to the cutoff: Sharp,
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Fuzzy RD Designs • Requires a dis
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Requirements for Estimating Treatme
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Implementation Threats to RD Design
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Example: AYP Data from Texas Densit
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What to do? • This is a big probl
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Analytic Threats to RD Design: Miss
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Nonlinearities in Functional Form
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Here we see a discontinuity between
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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
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Non‐parametric plot Local linear
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Semi‐Parametric Plot RD data from
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What to do? • Check to see whethe
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Shadish, Galindo, Wong, V., Steiner
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RD Design assumption 2 • Continui
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Implementation assumption 2 • No
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Analytic assumption 1 continued •
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Shadish et al. (under review) Vocab
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
Page 111 and 112: Generalizing Beyond The Cut‐off I
Page 113: Observed vs Counterfactual Outcomes
Page 117 and 118: What Do We Mean By Comparison Group
Page 119 and 120: Our Purpose • Use experimental da
Page 121 and 122: What Is The Target Parameter? • A
Page 127 and 128: What About The Above The Cut‐Off
Page 129 and 130: Root Mean Square Error In Experimen
Page 131 and 132: Post-Treatment Medicaid Expenditure
Page 133 and 134: Results For Number of Unpaid Worker
Page 135 and 136: Results For Expenditures With Expen
Page 137 and 138: Conclusions re Using a Comparison R
Page 139 and 140: Implications for Practice • Which
Page 141 and 142: RD with Multiple Cutoffs Design (as
Page 143 and 144: Analyzing RDs with Multiple Cutoffs
Page 145 and 146: Examples • Black, Galdo, and Smit
Page 147 and 148: Issue 2: To pool or not pool across
Page 149 and 150: Pooling is Already Common in Social
Page 151 and 152: What is Special about Pooling RD Es
Page 153 and 154: Estimating Treatment Effects across
Page 155 and 156: Estimating Treatment Effects across
Page 157 and 158: Generalizing Across Multiple Assign
Page 159 and 160: Multivariate RDD with Two Assignmen
Page 161 and 162: Recent Education Examples of RDDs w
Page 163 and 164: 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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