Selects the appropriate quasi-experimental method (DiD, ITS, SC) based on data structure and research questions. Use when the user is unsure which method to apply.
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Updated Jan 11, 2026, 09:59 PM
Why Use This
This skill provides specialized capabilities for pymc-labs's codebase.
Use Cases
Developing new features in the pymc-labs repository
Refactoring existing code to follow pymc-labs standards
Understanding and working with pymc-labs's codebase structure
---
name: designing-experiments
description: Selects the appropriate quasi-experimental method (DiD, ITS, SC) based on data structure and research questions. Use when the user is unsure which method to apply.
---
# Designing Experiments
Helps select the appropriate causal inference method.
## Decision Framework
1. **Control Group?**
* **Yes**: Go to Step 2.
* **No**: Consider **Interrupted Time Series (ITS)**.
2. **Unit Structure?**
* **Single Treated Unit**:
* With multiple controls: **Synthetic Control (SC)**.
* No controls: **ITS**.
* **Multiple Treated Units**:
* With control group: **Difference-in-Differences (DiD)**.
3. **Time Structure?**
* **Panel Data** (Multiple units over time): Required for DiD and SC.
* **Time Series** (Single unit over time): Required for ITS.
## Method Quick Reference
* **Difference-in-Differences (DiD)**: Compares trend changes between treated and control groups. Assumes **Parallel Trends**.
* **Interrupted Time Series (ITS)**: Analyzes trend/level change for a single unit after intervention. Assumes **Trend Continuity**.
* **Synthetic Control (SC)**: Constructs a synthetic counterfactual from weighted control units. Assumes **Convex Hull** (treated unit within range of controls).
