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Research Paper#Causal Inference, Machine Learning, Gaussian Processes🔬 ResearchAnalyzed: Jan 3, 2026 18:47

Scalable Heterogeneous Treatment Effect Estimation with Propensity Patchwork Kriging

Published:Dec 29, 2025 13:49
1 min read
ArXiv

Analysis

This paper addresses the computational limitations of Gaussian process-based models for estimating heterogeneous treatment effects (HTE) in causal inference. It proposes a novel method, Propensity Patchwork Kriging, which leverages the propensity score to partition the data and apply Patchwork Kriging. This approach aims to improve scalability while maintaining the accuracy of HTE estimates by enforcing continuity constraints along the propensity score dimension. The method offers a smoothing extension of stratification, making it an efficient approach for HTE estimation.
Reference

“The proposed method partitions the data according to the estimated propensity score and applies Patchwork Kriging to enforce continuity of HTE estimates across adjacent regions.”

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Paper#LLM🔬 ResearchAnalyzed: Jan 3, 2026 19:47

Selective TTS for Complex Tasks with Unverifiable Rewards

Published:Dec 27, 2025 17:01
1 min read
ArXiv

Analysis

This paper addresses the challenge of scaling LLM agents for complex tasks where final outcomes are difficult to verify and reward models are unreliable. It introduces Selective TTS, a process-based refinement framework that distributes compute across stages of a multi-agent pipeline and prunes low-quality branches early. This approach aims to mitigate judge drift and stabilize refinement, leading to improved performance in generating visually insightful charts and reports. The work is significant because it tackles a fundamental problem in applying LLMs to real-world tasks with open-ended goals and unverifiable rewards, such as scientific discovery and story generation.
Reference

“Selective TTS improves insight quality under a fixed compute budget, increasing mean scores from 61.64 to 65.86 while reducing variance.”

PermalinkArXiv