Regression-Discontinuity Design - Institute for Policy Research

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Root Mean Square Error In Experimental Benchmark vs Extrapolations Number of Unpaid Workers as a Function of Age Experimental Linear Extrapolation Partially Linear Model Polynomial Group 0 .2 .4 .6 .8 Square Root of Mean Square Error
Post-Treatment Medicaid Expenditures as a Function of Pre-Treatment Expenditures Root Mean Square Error In Experimental Benchmark vs Extrapolations Experimental Linear Extrapolation Partially Linear Model Polynomial Group 0 200 400 600 Square Root of Mean Square Error
Page 1 and 2:
Regression‐Discontinuity Design D
Page 3 and 4:
Design Overview Design Examples Vis
Page 5 and 6:
Real Examples of RD Reading First b
Page 7 and 8:
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
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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
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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
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RD Design assumption 2 • Continui
Page 57 and 58:
Implementation assumption 2 • No
Page 59 and 60:
Analytic assumption 1 continued •
Page 61 and 62:
Shadish et al. (under review) Vocab
Page 63 and 64:
RDD Example 2
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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
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What to do (5) • Move to a tie br
Page 75 and 76:
Example Cocaine Project • Include
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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 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: Root Mean Square Error In Experimen
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 )
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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