Summary of Causal Inference From Slowly Varying Nonstationary Processes, by Kang Du and Yu Xiang
Causal Inference from Slowly Varying Nonstationary Processes
by Kang Du, Yu Xiang
First submitted to arxiv on: 11 May 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 class of restricted structural causal models (SCMs) leverages asymmetry from nonstationarity in time series data to identify causal relationships in both bivariate and network settings. The approach uses a time-varying filter and stationary noise, and is particularly effective for slowly varying processes. Efficient procedures are developed by leveraging powerful estimates of the bivariate evolutionary spectra. The methodology is demonstrated on various synthetic and real datasets involving high-order and non-smooth filters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal inference from observational data is like trying to figure out what caused a problem in the past. Usually, we need special rules or patterns in the data to help us make sense of it. But what if the data is changing all the time? That’s where this new method comes in. It uses a special filter to sort through the noise and find the underlying cause-and-effect relationships. The result is a way to identify causes in both simple and complex situations, making it useful for things like predicting stock prices or understanding how diseases spread. |
Keywords
» Artificial intelligence » Inference » Time series
