Summary of General Frameworks For Conditional Two-sample Testing, by Seongchan Lee et al.
General Frameworks for Conditional Two-Sample Testing
by Seongchan Lee, Suman Cha, Ilmun Kim
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 investigates conditional two-sample testing, which aims to determine if two populations have the same distribution after accounting for confounding factors. This problem arises in applications like domain adaptation and algorithmic fairness, where comparing groups while controlling for confounding variables is essential. The authors establish a hardness result, demonstrating that no valid test can have significant power against any single alternative without proper assumptions. Two general frameworks are introduced: one converts conditional independence tests into conditional two-sample tests, preserving asymptotic properties; the other transforms the problem into comparing marginal distributions with estimated density ratios, leveraging existing methods for marginal two-sample testing. The authors demonstrate these frameworks using classification and kernel-based methods and conduct simulation studies to illustrate their finite-sample performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conditional two-sample testing is an important problem that helps determine if two groups have the same distribution after accounting for confounding factors. This is crucial in applications like domain adaptation and algorithmic fairness, where comparing groups while controlling for confounding variables is essential. The paper shows that no valid test can have significant power against any single alternative without proper assumptions. It then proposes two frameworks to solve this problem: one converts conditional independence tests into conditional two-sample tests, and the other transforms the problem into comparing marginal distributions with estimated density ratios. |
Keywords
» Artificial intelligence » Classification » Domain adaptation
