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arXiv papers explore advanced conditional independence testing methods · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

Three new research papers published on arXiv explore advanced methods for conditional independence testing, a fundamental problem in scientific discovery. The first paper, 'Embedding-Bias in Conditional Independence Testing,' addresses the validity issues that arise when using embeddings of data, proposing a robust test that accounts for discarded information. The second paper, 'Tight Bounds for Equivalence Testing with Non-Adaptive Conditional Samples,' provides precise theoretical bounds for testing if two distributions are equivalent using conditional samples. The third paper, 'Sequential Conditional Independence Testing with Machine Learning Models,' investigates how to integrate machine learning models into sequential testing frameworks, explaining why certain e-variable estimates can outperform others in practice and offering methods to reduce approximation and estimation errors. AI

IMPACT These papers advance theoretical understanding and practical methods for conditional independence testing, crucial for causal inference and model interpretability in AI.

RANK_REASON Cluster consists of three academic papers published on arXiv concerning statistical methods and machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

arXiv papers explore advanced conditional independence testing methods · 3 sources tracked

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Cluster consists of three academic papers published on arXiv concerning statistical methods and machine learning.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Nikolaj Thams, Anton Rask Lundborg ·

    Embedding-Bias in Conditional Independence Testing

    arXiv:2610.11584v1 Announce Type: cross Abstract: To test conditional independence of $X$ and $Y$ given a text or an image $Z$, one conditions on an embedding $\psi(Z)$ in place of $Z$. The embedded test is valid if $Z$ is independent of $X$ or of $Y$ given $\psi(Z)$, which canno…

  2. arXiv stat.ML TIER_1 English(EN) · Gautam Kamath ·

    Tight Bounds for Equivalence Testing with Non-Adaptive Conditional Samples

    arXiv:2610.11145v1 Announce Type: cross Abstract: We study distribution testing with access to non-adaptive conditional samples. Specifically, we give tight bounds for equivalence testing, determining whether two unknown distributions are equal to or $\varepsilon$-far from each o…

  3. arXiv stat.ML TIER_1 English(EN) · Angel Reyero-Lobo, Michele Meziu, Sebastian Uriel Arias, Peter Gr\"unwald ·

    Sequential Conditional Independence Testing with Machine Learning Models

    arXiv:2610.11388v1 Announce Type: cross Abstract: Conditional independence testing is a ubiquitous problem in scientific discovery. The widely employed model-X assumption shifts the modelling burden from the dependence of the output on the inputs to the dependencies within the in…

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