About me

I am a final-year PhD candidate at TU Delft supervised by Jesse Krijthe and Marcel Reinders in the Pattern Recognition Laboratory. As part of my PhD, I was also a visiting graduate student at Harvard University where I worked with Issa Dahabreh in the CAUSALab. Morever, I spent three months as a machine learning scientist intern at Booking.com in Amsterdam.

As I expect to graduate by the end of 2025, I am actively seeking research positions in academia and industry.

Although I sit in the computer science department, my research interests primarily lie in statistical methods, particularly at the intersection of causal inference and machine learning. My focus is on developing robust and reliable methods for prediction and decision-making. Much of my thesis explores causal inference in multi-source datasets, including methods for integrating external control data to enhance statistical inference in randomized experiments and developing falsification strategies to test the unconfoundedness assumption when having access to multiple observational datasets.

I am originally from Sweden, where I earned a BSc in Engineering Physics and an MSc in Engineering Mathematics from Chalmers University of Technology. During my studies, I also interned at NASA Goddard Space Flight Center. My full CV can be found here.

News

[Feb 2025] New preprint: Qini curve estimation under clustered network interference.

[Feb 2025] New preprint: Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms.

[Jan 2025] Became recipient of the G-Research grant for early-career researchers.

[Jul 2024] Starting my research internship at Booking.com in Amsterdam.

[Jun 2024] New preprint: Robust integration of external control data in randomized trials.

[Oct 2023] Paper accepted to NeurIPS 2023 workshop on Causal Representation Learning: You can read it here.

[Sep 2023] Paper accepted to NeurIPS 2023: Detecting hidden confounding in observational data using multiple environments.

[Sep 2023] Starting my research visit at Harvard University in the CAUSALab.

[Aug 2023] Paper on benchmarking surrogate-based optimation algorithms from my masters accepted to Applied Soft Computing. You can read it here.

[Apr 2023] Attended CLEAR 2023 in beautiful Tübingen, Germany.

[Sep 2022] Presented a poster at the ELLIS Doctoral Symposium in Alicante, Spain.

[Jun 2022] Presented at the Machine Learning Summer School in Krakow, Poland.

[Jan 2022] Paper accepted to AISTATS 2022: Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models.

[Sep 2021] Leaving Sweden to start my PhD at Delft University of Technology.

[Jul 2021] Got 1st place on the GECCO 2021 Industrial Challenge (limited evaluation track) together with Laurens Bliek and Arthur Guijt, our approach is described here.

[Jun 2021] Finished my thesis and graduated with a MSc in Engineering Mathematics from Chalmers University of Technology.

[Nov 2020] Paper accepted to BNAIC/BeneLearn 2020: Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems.

Research

The full list of papers can also be found on my Google scholar profile.

Preprints

• Rickard Karlsson, Jesse H. Krijthe Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms. Under review, 2025.
  [paper [code]
• Rickard Karlsson, Bram van den Akker, Felipe Moraes, Hugo M. Proença, Jesse H. Krijthe Qini curve estimation under clustered network interference. Under review, 2025.
  [paper [code]
• Rickard Karlsson*, Guanbo Wang*, Jesse H. Krijthe, Issa J. Dahabreh Robust integration of external control data in randomized trials. Under review, 2024. *Equal contribution
  [paper] [code]

Publications

• Rickard Karlsson, Jesse H. Krijthe Detecting Hidden Confounding in Observational Data using Multiple Environments. NeurIPS, 2023.
  [paper] [code]
• Rickard Karlsson, Ștefan Creastă, Jesse H. Krijthe Putting Causal Identification to the Test: Falsification using Multi-Environment Data. CLR Workshop, NeurIPS, 2023.
  [paper]
• Laurens Bliek, Arthur Guijt, Rickard Karlsson, Sicco Verwer, Mathijs de Weerdt Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions. Applied Soft Computing, 2023.
  [paper] [code]
• Rickard Karlsson*, Martin Willbo*, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. AISTATS, 2022. *Equal contribution
  [paper] [code]
• Rickard Karlsson, Laurens Bliek, Sicco Verwer, Mathijs de Weerdt Continuous Surrogate-based Optimization Algorithms are Well-suited for Expensive Discrete Problems. BNAIC/Benelearn, 2020.
  [paper]

Contact

You can contact me through email at r.k.a.karlsson{at}tudelft.nl, feel free to reach out for collaborations. I am also active on X (former Twitter).

Office

Room 6.E.040, Building 28 at Delft University of Technology
Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands