Summary of Causal Discovery in Semi-stationary Time Series, by Shanyun Gao et al.
Causal Discovery in Semi-Stationary Time Series
by Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents a novel approach to discovering causal relationships from observational time series without assuming stationarity, a common challenge in various fields such as retail sales, transportation systems, and medical science. The authors consider semi-stationary time series, where a finite number of different causal mechanisms occur sequentially and periodically across time. They propose a constraint-based, non-parametric algorithm called PCMCI_{} to identify the underlying causal graph with conditional independence (CI) tests. This algorithm can capture alternating and recurring changes in causal mechanisms and validate its effectiveness on both simulated and real-world climate datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in science by finding new ways to understand how things happen over time. Usually, we assume that things don’t change too much over time, but sometimes they do! The scientists came up with a special way to deal with these kinds of data, which is important for many fields like sales, transportation, and medicine. They created a new method to find the underlying causes of what’s happening, even when it changes over time. It works really well on fake data and real climate data too! |
Keywords
* Artificial intelligence * Time series
