Image by Pablo García Saldaña

Regression discontinuity designs

Regression discontinuity designs offer one of the most credible ways to draw causal conclusions. With my collaborators, I work both on extensions of the design so that it can be applied to more data sets, and on applications in practice. 

This research line receives financial support from the Dutch Research Council, under grant OCENW.M20.190.

A blog post (in Dutch) for a general audience can be found here.


An opportunity for causal inference presents itself when an intervention is assigned based on a cut-off, as is very common in medical decision-making. Suppose for example that patients aged 65 or younger receive treatment A and patients older than 65 receive treatment B. On average, patients aged 64 will be similar to patients aged 66 in all potentially confounding aspects like BMI or smoking status. So if we assign patients different treatments based on age alone and find that the outcomes for patients aged 64 are much better than those of patients aged 66, we may reasonably ascribe this difference to the intervention, and claim a causal effect. This is the core concept behind the regression discontinuity design.

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As part of the PhD project of Jan Jaap de Graeff, we are working on applying this design to data from the Dutch Arthroplasty Register.

With PhD student Julia Kowalska, funded by the ENW-M grant, we'll design several extensions of the regression discontinuity design so that it can be applied in more medically relevant settings.