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designing-experiments
by pymc-labs
A Python package for causal inference in quasi-experimental settings
⭐ 1,093🍴 91📅 Jan 21, 2026
SKILL.md
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
-
Control Group?
- Yes: Go to Step 2.
- No: Consider Interrupted Time Series (ITS).
-
Unit Structure?
- Single Treated Unit:
- With multiple controls: Synthetic Control (SC).
- No controls: ITS.
- Multiple Treated Units:
- With control group: Difference-in-Differences (DiD).
- Single Treated Unit:
-
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).
Score
Total Score
80/100
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