PhD student high-dimensional causal inference

PhD student high-dimensional causal inference

Published Deadline Location
20 Feb 3 Apr Amsterdam

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Job description

Causal conclusions are at the center of research, yet notoriously difficult to obtain. In this project we aim to go beyond correlations and develop new statistical methods to draw reliable causal conclusions from high-dimensional data. This PhD position is part of the ERC-funded project 'BayCause: high-dimensional nonparametric Bayesian causal inference'.

About your role
You will do four years of research in causal inference, culminating in a PhD thesis. The focus of the PhD trajectory is on developing new methods and theory. In this subproject of the larger ERC-project 'BayCause', you will take the graphical approach rooted in directed acyclic graphs (DAGs). We will work in a high-dimensional setting, meaning that there are many covariates / features relative to the amount of individuals for which we have data ('small n, large p'). You will investigate settings in which the DAG is partially or fully unknown, and will research whether Bayesian methods are suitable for reliable causal effect estimation, including appropriate uncertainty quantification. We will consider various outcomes, including continuous, binary and survival outcomes.

This PhD position is jointly supervised by dr. Stéphanie van der Pas (Amsterdam UMC, Epidemiology and Data Science) and dr. Sara Magliacane (UvA, Amsterdam Machine Learning Lab).

Your main task will be to do research. You are expected to present your research at national and international workshops and conferences. Additionally, we expect you to participate in the Amsterdam UMC's teaching duties and departmental events.


Amsterdam UMC


We are looking for a motivated PhD student, with the following qualifications:
  • Ability to develop new statistical methods. Typically this can be demonstrated by a master's degree in Mathematics, Statistics, Econometrics or Computer Science;
  • Ideally, you'll have knowledge of Causal Inference and Bayesian Statistics, but we will also consider applications from applicants without prior knowledge on these topics;
  • Excellent communication skills, in writing and presenting;
  • Proficient in R and/or Python.
Please use the motivation letter to explain to what extent you meet these requirements.

In your application, please make sure to include the following:
  • CV;
  • Motivation letter (max 2 pages);
  • Transcript (grades) of your master's degree;
  • Writing sample, preferably a (draft) master's thesis if available.
Please combine the motivation letter, transcript and writing sample into one pdf.

Conditions of employment

We offer a fully funded 4-year PhD position, including a travel budget to attend workshops and conferences. The contract will be initially for 12 months, and will be extended by another 36 months in case of a positive evaluation.
  • Salary schale OIO: € 2.789 tot € 3.536 gross at full time employment (depending on qualifications and experience);
  • Additional benefits including an 8.3% end-of-year bonus and 8% holiday pay;
  • Pension is accrued at the ABP. VUmc contributes a large part of the premium (70%);
  • In addition to excellent accessibility by public transport, we reimburse 75% of your OV travel costs (commuting costs);
  • For more information about our fringe benefits, please visit: employment conditions.


Amsterdam UMC

You will join the vibrant Big Statistics research group, which is part of the Epidemiology and Data Science department at the Amsterdam UMC. You can read more about the group on Big Statistics.

Your supervisors will be dr. Stéphanie van der Pas (Stephanie van der Pas) and dr. Sara Magliacane (Sara Magliacane).

We currently work one day a week from the AMC location and one day a week from the VUmc location, with the option to work the remaining three days from home or from the AMC location.


  • PhD
  • Health
  • max. 36 hours per week
  • €2789—€3536 per month
  • University graduate
  • 8061



Meibergdreef 9, 1105AZ, Amsterdam

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