TSCSMethods.jl Visual Guide

This page provides comprehensive visual diagrams illustrating the statistical methodology, user workflows, and package architecture of TSCSMethods.jl.

1. Statistical Methodology

Core TSCS Matching Methodology

The following diagram shows the complete flow of the time-series cross-sectional matching methodology:

Statistical Validation Framework

TSCSMethods.jl includes comprehensive validation to ensure statistical correctness:

2. User Workflows

Basic User Workflow

The standard workflow for using TSCSMethods.jl:

Advanced Workflows

Extended capabilities for sophisticated analyses:

Data Requirements & Validation

Input data structure and validation process:

3. Package Architecture

Module Structure & Dependencies

The clean modular organization of TSCSMethods.jl:

Type Hierarchy System

TSCSMethods.jl uses a clean object-oriented design with clear inheritance:

Abstract Base Types:

• VeryAbstractCICModel - Base abstract type for all models
• AbstractCICModel - For non-stratified models
• AbstractCICModelStratified - For stratified models

Concrete Implementations:

• CIC - Core implementation with matching, balancing, and estimation
• CICStratified - Stratified analysis with subgroup effects
• CaliperCIC - Constrained matching within distance thresholds
• RefinedCIC - Iterative match refinement capabilities

This hierarchy provides flexibility while maintaining type safety and clear interfaces.

Testing & Validation Architecture

TSCSMethods.jl includes a comprehensive quality assurance framework:

Test Categories:

• Unit Tests - Individual component testing by subsystem
• Integration Tests - End-to-end workflow validation
• Correctness Tests - Statistical validation with known outcomes
• Validation Gates - Automated quality thresholds

Statistical Validation Results:

• Coverage: 96% (within target 93-97%)
• Placebo: 6.87% Type I error (within target 3-7%)
• Benchmarks: Performance validated (all passing)

The validation framework ensures statistical correctness while maintaining high code quality standards.

Summary

These diagrams illustrate TSCSMethods.jl as a comprehensive, professionally-designed package for causal inference:

• Statistical Rigor: Validated methodology with comprehensive testing
• User-Friendly: Clear workflows from basic to advanced usage
• Professional Architecture: Clean modular design with 37 organized files
• Quality Assurance: 8,146 tests with statistical validation gates

The package successfully bridges rigorous statistical methodology with practical usability, making advanced causal inference methods accessible while maintaining the highest standards of statistical correctness.