Regression-Discontinuity Design - Institute for Policy Research

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Mis‐specification of Functional Form (Pre‐K Example) Sometimes, form is less clear Example: Oklahoma Print Awareness 0 10 20 30 40 0 10 20 30 40 -400 -200 0 200 400 Days from cutoff print awareness raw score Fitted values Linear plot print awareness raw score Fitted values -400 -200 0 200 400 Days from cutoff print awareness raw score lowess paraw cutmarg Lowess plot print awareness raw score lowess paraw cutmarg
Correct specification of functional form assumption What to do about functional form (1) • Graphical approaches
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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
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Two Rationales for RDD 1. Selection
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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 and 60: Analytic assumption 1 continued •
Page 61 and 62: Shadish et al. (under review) Vocab
Page 63 and 64: RDD Example 2
Page 65: Best case scenario - regression lin
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 and 114: Observed vs Counterfactual Outcomes
Page 115 and 116: 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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