Summary of On the Recoverability Of Causal Relations From Temporally Aggregated I.i.d. Data, by Shunxing Fan et al.
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
by Shunxing Fan, Mingming Gong, Kun Zhang
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 We examine how temporal aggregation affects instantaneous causal discovery in general settings, motivated by the observation that true causal time lags are often shorter than observational intervals. This discrepancy can lead to high aggregation, causing time-delay causality to vanish and instantaneous dependence to manifest. Although instantaneous dependence may be consistent with the true causal relation, it remains unclear what type of consistency is needed or when such consistency will hold. We proposed functional consistency and conditional independence consistency for functional causal model-based methods and conditional independence-based methods respectively, and provided conditions under which these consistencies will hold. Our findings show that causal discovery results can be seriously distorted by aggregation, especially in complete nonlinear cases, but may still be recoverable from aggregated data if there is partial linearity or an appropriate prior. This suggests that the community should take a cautious approach when interpreting causal discovery results from such data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how taking averages of data over time affects finding the causes behind certain events. They found that taking too many averages can hide the true cause-and-effect relationships and instead show unrelated events as connected. The researchers proposed ways to ensure that the average data still shows the correct relationships, but warned that even with these precautions, the results might be wrong if the real relationships are very complex. Overall, the study suggests being careful when interpreting findings based on aggregated data. |
