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A fundamental assumption of the RDD is that there is a discontinuous change in the probability of exposure at the assignment cut-off. Therefore, we first assessed whether discontinuity of exposure was present in our study.
In the Fuzzy Regression Discontinuity (FRD) design, the probability of receiving the. treatment needs not change from zero to one at the threshold. Instead, the design allows. for a smaller jump in the probability of assignment to the treatment at the threshold: lim.
Under an RD design, the effect on an intervention can be estimated as the difference in mean outcomes between treatment and comparison group units, adjusting statistically for the relationship between the outcomes and the variable used to assign units to the intervention, typically referred to as the \u201cforcing\u201d or \u201c ...
IES (2008) defines regression discontinuity designs as \u201cdesigns in which participants are assigned to the intervention and the control conditions based on a cut-off score on a pre-intervention measure that typically assesses need or merit.
The running variable. completely determines who gets treatment. We must observe X and know the cutoff or threshold c. In fuzzy RDD, we can think of D as a random variable given X, but. E[Di |Xi = c] is known to be discontinuous at c.
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Required assumptions. 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.
If the continuity, or exchangeability, assumption holds in a regression discontinuity study, then individuals whose measured values are immediately below the threshold can serve as a valid counterfactual for those immediately above the threshold, as the distribution of baseline covariates is expected to be the same in ...
Regression Discontinuity Design (RDD) is a quasi-experimental impact evaluation method used to evaluate programs that have a cutoff point determining who is eligible to participate.
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.
In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest-posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.

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