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designing-experiments
GitHub用于在数据分析前设计实验和准实验研究方案。帮助用户根据数据结构选择DiD、ITS或合成控制等方法，明确处理组、对照组及假设。同时提供失败实验的恢复策略，区分实施错误与设计缺陷，指导后续验证决策。
Trigger Scenarios
选择研究设计和实验结构
确定处理组与对照组设置
评估平行趋势等关键假设
科学实验失败后的恢复与验证规划
决定使用DiD、ITS或合成控制方法
Install
npx skills add foryourhealth111-pixel/Vibe-Skills --skill designing-experiments -g -y
SKILL.md
Frontmatter
{
"name": "designing-experiments",
"description": "Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment\/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead."
}
Designing Experiments
Helps choose and specify a research design before data analysis starts. This skill owns study-design decisions: what is treated, what is compared, what outcome is measured, which assumptions are required, which validation or recovery experiment should follow a failed scientific experiment, and which design is defensible.
It does not fit causal models, estimate treatment effects, interpret fitted model output from existing data, or debug software/build failures.
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).
Failed Experiment Recovery
When a scientific experiment or optimization plan produces weak or contradictory results, use the same design surface to:
- Separate implementation or measurement errors from design-assumption failures.
- Identify which assumption should be tested next.
- Define a minimal validation experiment before abandoning the approach.
- State the decision rule for continuing, revising, or stopping the line of work.
Version History
- f627ab5 Current 2026-07-25 04:40
