# Causal Discovery on PISA OECD trends 2022 Source: [Causal Discovery on PISA OECD trends 2022]() Collection index: [llms.txt]() Data updated: 2026-06-19T07:13:51.122Z Evaluation of Causal Discovery methods on the PISA OECD trends 2022 dataset, measured using metrics such as Directed F1, SHD, Skeleton F1, and Base F1. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Causal Discovery | | Dataset (including any stated split) | PISA OECD trends 2022 | | Metric | Directed F1 | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 0 of 0 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Directed F1](): Higher is better; unit: PERCENT. - [SHD](): Lower is better; unit: SCALAR. - [Skeleton F1](): Higher is better; unit: PERCENT. - [Base F1](): Higher is better; unit: PERCENT. ## Recorded results | Method | Directed F1 | Result date | Report paper | | --- | --- | --- | --- | Page 1 of 1. This is an index page, not the full collection.