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New arXiv papers explore causal discovery methods and conditional independence tests

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers delve into causal discovery, a field focused on uncovering causal relationships from data. The first paper introduces a method for interpretable causal discovery using causal-effect constraints, adapting rare-event estimation techniques to handle computational challenges. The second paper provides a survey of conditional independence tests, which are crucial for constraint-based causal discovery algorithms, organizing them into six families and discussing their assumptions, robustness, and scalability. AI

IMPACT These papers advance research in causal inference, potentially improving AI's ability to understand and explain complex systems.

RANK_REASON Two academic papers published on arXiv discussing causal discovery methods and conditional independence tests.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers explore causal discovery methods and conditional independence tests

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Cixuan Zhang, Guy Van den Broeck, Benjie Wang ·

    Interpretable Causal Discovery via Causal-Effect Constraints

    arXiv:2608.12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but also to be able to interpret and explain either obse…

  2. arXiv stat.ML TIER_1 English(EN) · Pavel Averin, Theodoros Moysiadis, Ioannis Katakis ·

    Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey

    arXiv:2608.11156v1 Announce Type: new Abstract: Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from …

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