Rickard Karlsson
PhD candidate in causal inference & machine learning
Delft University of Technology, the Netherlands
About me
I am a third-year PhD candidate at TU Delft supervised by Jesse Krijthe and Marcel
Reinders in the Pattern Recognition Laboratory. Currently, I am doing an internship as a machine learning
scientist at Booking.com in Amsterdam.
During my PhD have I also been fortunate to spend time at Harvard University in the CAUSALab working with Issa
Dahabreh.
My research interests are at the intersection of causal inference and machine learning, with the goal of
creating more robust and reliable methods for predictions and decision-making from data.
Most recently, I have been interested in causal inference problems where we have access to datasets from
different sources, such as different
studies or experiments, which can improve the robustness and statistical inference of causal analyses.
Originally, I come from Sweden where I finished my BSc in physics and MSc in mathematics at Chalmers
University
of Technology. I also got the opportunity to do an internship at NASA Goddard Space
Flight Center during my studies.
My full CV can be found here.
And when not doing research, I enjoy endurance sports, CrossFit, and trekking in nature.
News
Publications
The list of publications can also be found on my Google scholar profile.
-
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] -
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.
Causal Representation Learning 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).