Causal Learning in Biomedical Applications: A Benchmark
Learning causal relationships between a set of variables is a challenging problem in computer science. Many existing artificial benchmark datasets are based on sampling from causal models and thus contain residual information that the ${R} ^2$-sortability can identify. Here, we present a benchmark f...
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| creator | Ryšavý, Petr He, Xiaoyu Mareček, Jakub |
| description | Learning causal relationships between a set of variables is a challenging
problem in computer science. Many existing artificial benchmark datasets are
based on sampling from causal models and thus contain residual information that
the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods
in causal learning using time series. The presented dataset is not
${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that
is used in cells to release energy. We provide four scenarios of learning,
including short and long time series, and provide guidance so that testing is
unified between possible users. |
| doi_str_mv | 10.48550/arxiv.2406.15189 |
| format | Article |
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problem in computer science. Many existing artificial benchmark datasets are
based on sampling from causal models and thus contain residual information that
the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods
in causal learning using time series. The presented dataset is not
${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that
is used in cells to release energy. We provide four scenarios of learning,
including short and long time series, and provide guidance so that testing is
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problem in computer science. Many existing artificial benchmark datasets are
based on sampling from causal models and thus contain residual information that
the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods
in causal learning using time series. The presented dataset is not
${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that
is used in cells to release energy. We provide four scenarios of learning,
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problem in computer science. Many existing artificial benchmark datasets are
based on sampling from causal models and thus contain residual information that
the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods
in causal learning using time series. The presented dataset is not
${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that
is used in cells to release energy. We provide four scenarios of learning,
including short and long time series, and provide guidance so that testing is
unified between possible users.</abstract><doi>10.48550/arxiv.2406.15189</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning |
| title | Causal Learning in Biomedical Applications: A Benchmark |
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