4 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…
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…
Towards Causal Credit Assignment
Mátyás Schubert
Adequately assigning credit to actions for future outcomes based on their contributions is a long-standing open challenge in Reinforcement Learning. The assumptions of the most com…