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Local discovery by partitioning: polynomial-time causal discovery around exposure-outcome pairs
Causal discovery is crucial for causal inference in observational studies, as it can enable the
identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global …
identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global …
Parallel execution of causal structure learning on graphics processing units
C Hagedorn - 2023 - publishup.uni-potsdam.de
Learning the causal structures from observational data is an omnipresent challenge in data
science. The amount of observational data available to Causal Structure Learning (CSL) …
science. The amount of observational data available to Causal Structure Learning (CSL) …
[PDF][PDF] Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery
J Hügle - 2024 - researchgate.net
Abstract Knowledge about causal structures is crucial for decision support in various
domains. For example, in discrete manufacturing, identifying the root causes of failures and …
domains. For example, in discrete manufacturing, identifying the root causes of failures and …