svy-sae: Small Area Estimation in Python

Official documentation for svy-sae, a comprehensive Python package for Small Area Estimation (SAE). Learn area-level and unit-level modeling techniques.
Author

svyLab

Published

January 8, 2026

Keywords

small area estimation, SAE, Pythonic, statistics, survey sampling

API Stability

svy-sae provides a stable core API for small area estimation, including unit-level and area-level models, benchmarking, and design-consistent extensions.

While the surrounding ecosystem (additional features, performance backends, diagnostics, and documentation) continues to evolve, the primary modeling and estimation interfaces are expected to remain fairly stable.

📧 Feedback welcome: info@svylab.com
🐛 Report issues: GitHub Issues

Overview

svy-sae is the Python package for scalable Small Area Estimation (SAE). It brings rigorous area-level and unit-level modeling workflows to the Python scientific ecosystem, filling the gap for official statistics and applied research.

The package emphasizes practical estimation, reproducibility, and high performance, leveraging the svy ecosystem for design consistency and JAX for computational speed.

Key Capabilities

  • Area-Level Models: Fay–Herriot implementations with robust variance estimation (EBLUP, Hierarchical Bayes).
  • Unit-Level Models: Battese-Harter-Fuller (BHF) and extensions for fine-grained granular inference.
  • Design Consistency: Native integration with complex survey weights and direct estimates from svy.
  • Performance: Accelerated fitting and prediction using JAX backends.
  • Diagnostics: Built-in tools for uncertainty estimation, benchmarking to national totals, and model validation.

Documentation