example-datasets
Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes. Use when the user needs sample data or asks which demo datasets are available.
소스 정보
- 저장소
- pymc-labs/CausalPy
- 최근 소스 활동
- 2026년 6월 8일 13:06
- 감지된 SKILL.md 언어
- 영어
- 스타
- 1,190
- 포크
- 114
설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SKILL.md 표시 중
SKILL.md
소스 지침 · 읽기 전용 미리보기- name
- example-datasets
- description
- Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes. Use when the user needs sample data or asks which demo datasets are available.
# Example Datasets
CausalPy ships with built-in datasets that can be loaded with `cp.load_data(...)`.
## Usage
```python
import causalpy as cp
df = cp.load_data("did")
```
## Available Datasets
| Key | Typical use | Description |
|---|---|---|
| `"did"` | Difference-in-differences | Synthetic DiD example data |
| `"banks"` | Difference-in-differences | Historic banking closures data |
| `"its"` | Interrupted time series | Seasonal synthetic ITS data |
| `"its simple"` | Interrupted time series | Simplified synthetic ITS data |
| `"covid"` | Interrupted time series | Deaths and temperature data for England and Wales |
| `"sc"` | Synthetic control | Synthetic control example data |
| `"brexit"` | Synthetic control | UK GDP data for Brexit causal impact |
| `"california_prop99"` | Synthetic control | California Proposition 99 cigarette sales panel |
| `"rd"` | Regression discontinuity | Synthetic RD example data |
| `"drinking"` | Regression discontinuity | Minimum legal drinking age data |
| `"geolift1"` | Geo experiments | Single-treatment geo-lift data |
| `"geolift_multi_cell"` | Geo experiments | Multi-cell geo-lift data |
| `"anova1"` | PrePostNEGD | Pre/post nonequivalent groups example |
| `"risk"` | Instrumental variables | Acemoglu, Johnson, and Robinson institutions data |
| `"schoolReturns"` | Instrumental variables | Schooling returns data |
| `"nhefs"` | Inverse propensity weighting | National Health and Nutrition Examination Survey data |
| `"lalonde"` | Inverse propensity weighting | LaLonde propensity-score data |
| `"nets"` | Inverse propensity weighting | National Supported Work Demonstration data |
| `"pisa18"` | General examples | PISA 2018 sample data |
| `"nevo"` | General examples | Berry, Levinsohn, and Pakes cereal data |
| `"zipcodes"` | Geo experiments | Zipcode-level geo-experiment data |
## Guidance
- Prefer these bundled datasets for examples and docs instead of fetching data at runtime.
- For method selection, use `choosing-causalpy-methods` after identifying the data shape.
- For fitting and plotting, use `running-causalpy-experiments`.
GitHub에서 보기