Regression-Discontinuity Design - Institute for Policy Research

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Results For Expenditures With Age Based Assignment <strong>Design</strong> Benchmark Bootstrap Bias Bootstrap SD Percent Bias Root MSE Experimental 6800.21 2.17 182.50 0.00 0.07 Comparison Group Semi‐Parametric Model 6800.21 2194.96 1011.61 0.32 2416.86 Comparison Group Parametric Model 6800.21 2238.94 971.78 0.33 2440.74 No Comparison Group ‐‐ Linear Extrapolation 6800.21 2716.04 3538.79 0.40 4460.93
Conclusions re Using a Comparison RD to add to Generalization • Non‐equivalent comparison group often reduces bias relative to pure functional <strong>for</strong>m extrapolations. • Huge gains in statistical precision. • In terms of MSE the reductions in variance swamp the effects of bias. • Non‐equivalent comparison groups are a useful way of studying the consistency of causal effects beyond the cut‐off.
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Regression‐Discontinuity Design D
Page 3 and 4:
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
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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) • State‐of‐art
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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
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2. Overrides to the cutoff • Mult
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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 and 114: Observed vs Counterfactual Outcomes
Page 115 and 116: Questions About Generalizing From R
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: Results For Expenditures With Expen
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 )
Page 165 and 166: Requirements for a valid Multivaria
Page 167 and 168: Frontier Approach Estimates the dis
Page 169 and 170: Univariate Approach Addresses dimen
Page 171 and 172: Monte Carlo Study Wong, V., Steiner
Page 173: Implications for Practice • Which
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