Summary of The Landscape Of Causal Discovery Data: Grounding Causal Discovery in Real-world Applications, by Philippe Brouillard et al.
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
by Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 This paper presents a systematic review of recent causal discovery literature, highlighting limitations in current methods and applications. It critiques existing approaches for relying on unrealistic assumptions and inadequate evaluation metrics, often tested only on simple synthetic toy datasets. The paper showcases potential real-world applications in biology, neuroscience, and Earth sciences, where causal discovery can help address key challenges. It also discusses assumption violations that have driven the development of new methods and emphasizes the importance of using realistic datasets and adequate evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how scientists try to figure out cause-and-effect relationships from data. Right now, these efforts are limited because most methods make unrealistic assumptions or only test their ideas on simple fake datasets. The authors want to change this by showing what’s wrong with current approaches and highlighting the potential for applying causal discovery in fields like biology, neuroscience, and Earth sciences. They also discuss common problems that have led to new method developments. |
