90 citations · 201 across the 10 of their papers we have counts for
10 papers
Integrating Background Knowledge for Scalable Causal Discovery
Mátyás Schubert, Theofanis Aslanidis, Tom Claassen +1
Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identi…
Local Causal Discovery for Statistically Efficient Causal Inference
Mátyás Schubert, Tom Claassen, Sara Magliacane
Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global ca…
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
Carlos Cinelli, Avi Feller, Guido Imbens +3
Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer scienc…
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
Mátyás Schubert, Tom Claassen, Sara Magliacane
Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not…
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…
An Upper Bound for Random Measurement Error in Causal Discovery
Tineke Blom, Anna Klimovskaia, Sara Magliacane +1
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most like…
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen +3
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test…
Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, Tom Claassen
The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations bet…
Ancestral Causal Inference
Sara Magliacane, Tom Claassen, Joris M. Mooij
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the r…