Regression-Discontinuity Design - Institute for Policy Research

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Addressing Potential Issues with using Age‐Related Cutoffs • Differential entrance and attrition time in TR and CO groups – Because RD study lacks an initial defined sample, cannot assess attrition and selection effects – The issue then is: Are there compositional differences between children who make it to the fall of their kindergarten year (treatment group) and children who are just starting pre‐K (control group) • Particularly troubling concern because it could introduce serious bias in the design and make the approach invalid – Prompting some critics to ask, Is this an RD design at all?
IS Wong et al. a Fallible RD or Institutional Cohort Design? Fallible RD Institutional Cohort Design Children’s birthdates and state cutoffs are a mostly deterministic mechanism for treatment assignment for the subpopulation of units near the cutoff, but should probe for selection effects. The RD focuses on the comparability of units at the cutoff Leads the researcher to look for discontinuities in potential outcomes at the cutoff Assume that children’s birthdates and state cutoffs are not a deterministic mechanism for treatment assignment, so we should regard this design as an institutional cohort design approach. An institutional cohort design focuses on the comparability of all 5‐year‐olds in kindergarten versus all 4‐year‐old cohorts in pre‐K Leads the researcher to consider matching methods between cohorts to improve internal validity (will address later) …
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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
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 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: Addressing Potential Issues with us
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 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
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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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