5 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…
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…
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…