Summary of Challenges and Considerations in the Evaluation Of Bayesian Causal Discovery, by Amir Mohammad Karimi Mamaghan et al.
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
by Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Bayesian approach to causal discovery is proposed, focusing on representing uncertainty in experimental design. The Bayesian Causal Discovery (BCD) method estimates a posterior distribution, making it challenging to evaluate its quality. Existing metrics have been proposed, but there is no consensus on the most suitable one. This paper reexamines these metrics, dissecting their limitations and evaluating their performance through extensive empirical tests. The results show that many existing metrics fail to accurately assess the quality of approximation to the true posterior, particularly in scenarios with low sample sizes where BCD is desired. The study highlights the suitability (or lack thereof) of these metrics under different factors, including identifiability of the underlying causal model and quantity of available data. These findings emphasize the importance of nuanced evaluation procedures for this challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal discovery is a crucial step in experimental design and decision-making. This paper explores how to accurately evaluate Bayesian Causal Discovery (BCD) methods. BCD estimates a posterior distribution, which makes it hard to figure out if the method is working well or not. Researchers have proposed different metrics to measure the quality of this estimate, but nobody agrees on what’s best. The authors look at these metrics again, trying to understand their strengths and weaknesses. They test each metric using lots of data and find that most don’t work well, especially when there isn’t much data available. This study shows why we need a better way to evaluate BCD methods and helps us create more accurate ones in the future. |
