تقرير
Metrics on Markov Equivalence Classes for Evaluating Causal Discovery Algorithms
| العنوان: | Metrics on Markov Equivalence Classes for Evaluating Causal Discovery Algorithms |
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| المؤلفون: | Wahl, Jonas, Runge, Jakob |
| سنة النشر: | 2024 |
| المجموعة: | Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Statistics - Machine Learning |
| الوصف: | Many state-of-the-art causal discovery methods aim to generate an output graph that encodes the graphical separation and connection statements of the causal graph that underlies the data-generating process. In this work, we argue that an evaluation of a causal discovery method against synthetic data should include an analysis of how well this explicit goal is achieved by measuring how closely the separations/connections of the method's output align with those of the ground truth. We show that established evaluation measures do not accurately capture the difference in separations/connections of two causal graphs, and we introduce three new measures of distance called s/c-distance, Markov distance and Faithfulness distance that address this shortcoming. We complement our theoretical analysis with toy examples, empirical experiments and pseudocode. Comment: Under review. Comments welcome. Additional references added. Figure arrangement in appendix fixed |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.04952 |
| رقم الأكسشن: | edsarx.2402.04952 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |