How does PolicyEngine handle population weighting?

PolicyEngine uses MicroDataFrames that automatically respect survey weights during operations like .mean(), .sum(), and .groupby(), ensuring results represent the total population.

What is the primary benefit of using the ensure() method?

The ensure() method implements smart caching by checking in-memory LRU caches and local disk storage before running a full simulation, significantly speeding up iterative development.

What entity levels are supported in the UK and US models?

Both support person and household levels. The UK model includes benefit units (benunit), while the US model includes tax_unit, spm_unit, family, and marital_unit.

Can I map data from the household level down to the person level?

Yes, using map_to_entity() with how='project', you can broadcast household-level values (like rent) to every individual member within that household.

PolicyEngine Simulation Mechanics

Name: PolicyEngine Simulation Mechanics
Author: PolicyEngine

byPolicyEngine

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数据科学与机器学习

Optimizes and manages PolicyEngine microsimulations with advanced caching, data access, and entity mapping patterns.

关于

This skill provides specialized guidance for working with the policyengine.py microsimulation framework. it helps developers and analysts master the simulation lifecycle, focusing on efficient caching via ensure(), accessing structured results through output_dataset.data, and performing complex cross-entity transformations with map_to_entity(). By implementing these patterns, users can build high-performance policy models that handle survey weights automatically and accurately compare baseline scenarios against complex tax and benefit reforms.

主要功能

Multi-level entity data access for individuals, households, and tax units
Smart caching management using memory and disk persistence with ensure()
2 GitHub stars
Advanced entity mapping for aggregating or broadcasting variables across levels
Standardized patterns for comparing baseline and reform simulation results
Automatic survey weighting for statistically accurate population analysis

使用场景

Aggregating individual-level fiscal variables to household or benefit unit totals
Analyzing the distributional impact of tax reforms across different demographic groups
Building iterative microsimulation scripts that require fast re-runs during development

关于

主要功能

Multi-level entity data access for individuals, households, and tax units
Smart caching management using memory and disk persistence with ensure()
2 GitHub stars
Advanced entity mapping for aggregating or broadcasting variables across levels
Standardized patterns for comparing baseline and reform simulation results
Automatic survey weighting for statistically accurate population analysis

使用场景

Aggregating individual-level fiscal variables to household or benefit unit totals
Analyzing the distributional impact of tax reforms across different demographic groups
Building iterative microsimulation scripts that require fast re-runs during development