Summary of Decor: Deconfounding Time Series with Robust Regression, by Felix Schur et al.
DecoR: Deconfounding Time Series with Robust Regression
by Felix Schur, Jonas Peters
First submitted to arxiv on: 11 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 The proposed paper addresses a challenging problem in causal inference on time series data, specifically when there are unobserved confounders present. The authors introduce Deconfounding by Robust regression (DecoR), a novel approach that leverages robust linear regression in the frequency domain to estimate the causal effect between two time series. DecoR builds upon two different robust regression techniques and improves existing bounds on estimation error without requiring distributional assumptions on the covariates. The authors demonstrate DecoR’s effectiveness through experiments on both synthetic and real-world data from Earth system science, including simulations that suggest its robustness to model misspecification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about using math and statistics to understand how things change over time. It’s trying to figure out what causes something to happen when we can’t see all the factors at play. The scientists developed a new method called DecoR, which helps us get more accurate answers by looking at the data in a special way. They tested it on some real-world data and showed that it works well, even if our initial assumptions aren’t exactly right. |
Keywords
» Artificial intelligence » Inference » Linear regression » Regression » Time series
