Summary of Practical Kernel Tests Of Conditional Independence, by Roman Pogodin et al.
Practical Kernel Tests of Conditional Independence
by Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach to statistical testing of conditional independence offers a data-efficient solution using kernel-based methods. The key challenge lies in balancing test power with the correct test level, where bias in the test statistic can lead to excessive false positives. To address this issue, three methods for bias control are introduced, leveraging data splitting, auxiliary data, and simpler function classes. These combined strategies demonstrate effectiveness on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to accurately test whether two things are independent when there’s something else involved. Right now, it’s hard to get the right balance between being too strict or too loose in our testing. The problem lies in the way we calculate the test result, which can be biased and lead to false positives. To fix this, three new methods are introduced that use different ways to split the data, add extra information, and simplify things. These approaches work well on made-up and real-world data. |
