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.
Key Takeaways
- •Addresses the computational challenges of Gaussian process models for HTE estimation.
- •Introduces Propensity Patchwork Kriging, a novel method for scalable HTE estimation.
- •Leverages propensity scores for data partitioning and continuity enforcement.
- •Offers a smoothing extension of stratification for efficient 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.”