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New Causal Inference Bounds Developed with GPT-5.6 Sol on arXiv

By PulseAugur Editorial · [1 sources] ·

A new paper on arXiv introduces sharp minimax bounds for determining causal direction in bivariate LiNGAM models. The research quantifies the sample complexity required, considering factors like edge strength, distance from Gaussianity, and disturbance scales. Notably, the proof for this paper was generated with GPT-5.6 Sol within OpenAI's Codex, with human oversight for verification and refinement. AI

IMPACT Demonstrates AI's capability in complex mathematical proof generation, potentially accelerating scientific discovery.

RANK_REASON Academic paper published on arXiv detailing new theoretical bounds for causal inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Causal Inference Bounds Developed with GPT-5.6 Sol on arXiv

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Academic paper published on arXiv detailing new theoretical bounds for causal inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jikai Jin ·

    How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM

    arXiv:2608.15840v1 Announce Type: cross Abstract: We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quan…

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