Summary of Standardizing Structural Causal Models, by Weronika Ormaniec et al.
Standardizing Structural Causal Models
by Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The proposed paper introduces internally-standardized structural causal models (iSCMs) as a modification of existing synthetic datasets generated by structural causal models (SCMs). The authors identify limitations in popular algorithms that exploit artifacts such as variances and correlations increasing along the causal ordering. To address this, iSCMs introduce a standardization operation at each variable during the generative process, making them not Var-sortable or R^2-sortable for commonly-used graph families. The paper also proves that linear iSCMs are less identifiable from prior knowledge on weights and do not collapse to deterministic relationships in large systems, potentially making iSCMs useful beyond benchmarking problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new type of dataset called internally-standardized structural causal models (iSCMs) to help with understanding cause-and-effect relationships. They noticed that existing datasets have some flaws, like patterns that can make it seem like there are more connections between variables than really exist. To fix this, the iSCMs are designed so that they don’t follow these patterns, making them a better tool for studying real-world situations. |
