Tineke Blom

Tineke Blom

PhD Student at University of Amsterdam

About me

I'm a PhD student at University of Amsterdam, under the supervision of Joris Mooij. My research centers around causal inference and graphical modelling. Specifically, I analyze the relationship between causal models and dynamical systems in the context of causal discovery. My interests are: causal inference, graphical models, structure learning, dynamical systems, and emergent phenomena.

I’m also an instructor for the master courses: Machine learning 1 and 2, as well as a supervisor for bachelor and master projects.

Interests

  • Causal inference
  • Graphical models
  • Structure learning
  • Dynamical systems
  • Emergent phenomena

Education

  • PhD in Causality

    University of Amsterdam

  • MSc in Mathematics, 2015

    Utrecht University

  • BSc in Physics, 2012

    Utrecht University

  • BSc in Mathematics, 2012

    Utrecht University

Publications & Preprints

Conditional independences and causal relations implied by sets of equations,
Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij,
Preprint (2020).
PDF arXiv
Beyond structural causal models: Causal constraints models,
Tineke Blom, Stephan Bongers, Joris M. Mooij,
Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence (UAI-2019).
PDF arXiv Proceedings
An upper bound for random measurement error in causal discovery,
Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij,
Proceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI-2018).
PDF arXiv Proceedings
Top down stress testing: An application of adaptive lasso to forecasting credit loss rates,
Tineke Blom,
Master thesis.
PDF

Talks & Presentations

Beyond structural causal models: causal constraints models,
Conference on Uncertainty in Artificial Intelligence in Tel Aviv (2019),
Oral presentation.
Link
Causal modeling in dynamical systems using causal constraint models,
Machine Learning Group colloquium at Radboud University Nijmegen (2018),
Oral presentation.
Link
Generalized structural causal models,
Conference on Uncertainty in Artificial Intelligence Causal workshop in Monterrey (2018),
Poster presentation.
Link
Causal discovery in the presence of measurement error,
Conference on Uncertainty in Artificial Intelligence in Monterrey (2018),
Poster presentation.
Link
Causal modeling for dynamical systems using generalized structural causal models,
Amsterdam Machine Learning Lab seminar (2018),
Oral presentation.
Link
Causal discovery in the presence of measurement error,
Masscausal workshop in Crete (2017),
Oral presentation.
Link
Causal discovery in the presence of measurement error,
Amsterdam Machine Learning Lab seminar (2017),
Oral presentation.
Link

Contact