Research

For a list of my publications, please see my Google Scholar profile

Research mission

I aim to increase the number of data sets from which we can draw trustworthy causal conclusions. To achieve this goal, I develop new causal inference methods on a strong mathematical foundation.

To facilitate the deployment of the new methods in practice, I maintain several open source R software packages, see Software.

Research interests

  • Causal inference

  • Bayesian nonparametrics

  • Variable selection

  • Survival analysis

  • Regression trees and forests

Videos about my research: DutchEnglish

Blog for a general audience (in Dutch): here.

Selected projects

Regression discontinuity designs

Extensions of the regression discontinuity design, which is one of the most credible ways to draw causal conclusions. With application to arthroplasty data.

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RD_illustration_with_effect.tiff

Caliper matching

We derive asymptotic properties of caliper matching, a form of propensity score matching.

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More info coming soon

Image by Ag PIC

Bayesian Additive Regression Trees

Posterior contraction results for Bayesian regression trees and forests.

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RegressionTree.png

Competing risks in orthopedic data

Guidelines for working with arthroplasty data, taking into account the competing risks structure and the dependence between various joints.

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Image by svetjekolem

Merged block randomisation

A novel restricted randomisation method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicenter trials. It can be used for more than two groups and unequal randomisation ratios.

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Mergedblocks.png

Detecting syntactic differences

Using the minimum description length principle, as well as other approaches, we developed tools to automatically detect syntactic differences between languages based on parallel corpora. 

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Image by Joshua Hoehne

Sensitivity analysis for missing outcomes

Bernstein-von Mises results for a Bayesian sensitivity analysis method for data where some outcomes are missing, possibly not at random.

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More info coming soon

Image by Brett Jordan

Shrinkage priors

Posterior contraction theorems, results on uncertainty quantification, variable selection and more for shrinkage priors, in particular for the popular horseshoe prior.

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Bayesian survival analysis

Bernstein-von Mises results in the supremum norm for various survival objects, justifying the use of credible bands to quantify the uncertainty in the survival function.

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Cancer_surv_blank.tiff

Cumulative sum charts

Developing a new monitoring tool for survival outcomes, with applications to arthroplasty data. In addition, development of cumulative sum charts for liver procurement data.

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Cusum_simplified.png

Bayesian community detection

We introduce a Bayesian estimator of the underlying class structure in the stochastic block model, when the number of classes is known, and show strong consistency.

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CommunityDetection.png