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Regression Discontinuity Design Marcelo Coca Perraillon University of Chicago April 24 29 2013 1 / 40 Introduction Basics Method developed to estimate treatment e ects in non-experimental settings Provides causal estimates of treatment e ects Good internal validity some assumptions can be empirically veri ed Treatment e ects are local LATE Limits external validity Easy to estimate First application Thistlethwaite and Campbell 1960 2 / 40 Thistlethwaite and Campbell They studied the impact of...
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Regression Discontinuity Design (RDD) is a quasi-experimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution.
The main strength of regression discontinuity is that it provides a transparent method to estimate causal effects of treatments or policies, particularly when randomized controlled trials are not possible.
Here's his example of regression discontinuity design: Say a district is offering a school turnaround intervention to all schools performing below a certain cutoff point. Comparing all the schools below that cutoff with all the schools above it would not be a rigorous study, because the groups are too different.
Regression discontinuity designs can be applied in situations where a threshold rule is used to determine treatment assignment. In this situation, a continuous variable (the assignment variable) is used, at least in part, to determine whether or not an individual is assigned to a treatment.
Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur.
One of the primary advantages of the regression-discontinuity design is that it can sometimes be implemented in situations in which a randomized experiment cannot be used.

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