4 citations · 9 across the 9 of their papers we have counts for
10 papers
The Minimal Search Space for Conditional Causal Bandits
Francisco N. F. Q. Simoes, Itai Feigenbaum, Mehdi Dastani +1
Causal knowledge can be used to support decision-making problems. This has been recognized in the causal bandits literature, where a causal (multi-armed) bandit is characterized by…
Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams
Thijs van Ommen
For causal discovery in the presence of latent confounders, constraints beyond conditional independences exist that can enable causal discovery algorithms to distinguish more pairs…
Fundamental Properties of Causal Entropy and Information Gain
Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen
Recent developments enable the quantification of causal control given a structural causal model (SCM). This has been accomplished by introducing quantities which encode changes in…
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
Causal Entropy and Information Gain for Measuring Causal Control
Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen
Artificial intelligence models and methods commonly lack causal interpretability. Despite the advancements in interpretable machine learning (IML) methods, they frequently assign i…
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
Thijs van Ommen, Mathias Drton
The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix. However, these pol…
Robust Probability Updating
Thijs van Ommen, Wouter M. Koolen, Thijs E. Feenstra +1
This paper discusses an alternative to conditioning that may be used when the probability distribution is not fully specified. It does not require any assumptions (such as CAR: coa…
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
Peter Grünwald, Thijs van Ommen
We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian r…
Combining predictions from linear models when training and test inputs differ
Thijs van Ommen
Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs…