12,321 research outputs found
Regression Discontinuity Designs Using Covariates
We study regression discontinuity designs when covariates are included in the
estimation. We examine local polynomial estimators that include discrete or
continuous covariates in an additive separable way, but without imposing any
parametric restrictions on the underlying population regression functions. We
recommend a covariate-adjustment approach that retains consistency under
intuitive conditions, and characterize the potential for estimation and
inference improvements. We also present new covariate-adjusted mean squared
error expansions and robust bias-corrected inference procedures, with
heteroskedasticity-consistent and cluster-robust standard errors. An empirical
illustration and an extensive simulation study is presented. All methods are
implemented in \texttt{R} and \texttt{Stata} software packages
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data
The regression discontinuity (RD) design is a quasi-experimental design that
estimates the causal effects of a treatment by exploiting naturally occurring
treatment rules. It can be applied in any context where a particular treatment
or intervention is administered according to a pre-specified rule linked to a
continuous variable. Such thresholds are common in primary care drug
prescription where the RD design can be used to estimate the causal effect of
medication in the general population. Such results can then be contrasted to
those obtained from randomised controlled trials (RCTs) and inform prescription
policy and guidelines based on a more realistic and less expensive context. In
this paper we focus on statins, a class of cholesterol-lowering drugs, however,
the methodology can be applied to many other drugs provided these are
prescribed in accordance to pre-determined guidelines. NHS guidelines state
that statins should be prescribed to patients with 10 year cardiovascular
disease risk scores in excess of 20%. If we consider patients whose scores are
close to this threshold we find that there is an element of random variation in
both the risk score itself and its measurement. We can thus consider the
threshold a randomising device assigning the prescription to units just above
the threshold and withholds it from those just below. Thus we are effectively
replicating the conditions of an RCT in the area around the threshold, removing
or at least mitigating confounding. We frame the RD design in the language of
conditional independence which clarifies the assumptions necessary to apply it
to data, and which makes the links with instrumental variables clear. We also
have context specific knowledge about the expected sizes of the effects of
statin prescription and are thus able to incorporate this into Bayesian models
by formulating informative priors on our causal parameters.Comment: 21 pages, 5 figures, 2 table
Regression discontinuity design with covariates
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be constructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.Treatment effect, causal effect, complier, LATE, nonparametric regression, endogeneity
Regression discontinuity design with covariates
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be Cconstructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.
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