loading-datasets
Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos.
When & Why to Use This Skill
This Claude skill provides seamless access to internal CausalPy example datasets, designed to streamline causal inference modeling and statistical analysis. By automating the data loading process, it allows researchers and data scientists to quickly prototype models for Difference-in-Differences (DiD), Synthetic Control, and Interrupted Time Series (ITS) using verified, real-world, and generic datasets.
Use Cases
- Educational Learning: Quickly load datasets like 'drinking' or 'brexit' to practice and understand complex causal inference methods such as Regression Discontinuity or Synthetic Control.
- Model Benchmarking: Use standardized datasets like 'geolift' or 'covid' to test the accuracy and performance of custom causal models against established baselines.
- Code Prototyping: Rapidly initialize analysis scripts with generic 'did' or 'its' data to verify code logic and visualization workflows before applying them to proprietary datasets.
- Demonstration and Documentation: Generate reproducible examples and demos for team presentations or technical documentation using the built-in CausalPy library examples.
| name | loading-datasets |
|---|---|
| description | Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos. |
Loading Datasets
Loads example datasets provided with CausalPy.
Usage
import causalpy as cp
df = cp.load_data("dataset_name")
Available Datasets
| Key | Description |
|---|---|
did |
Generic Difference-in-Differences |
its |
Generic Interrupted Time Series |
sc |
Generic Synthetic Control |
banks |
DiD (Banks) |
brexit |
Synthetic Control (Brexit) |
covid |
ITS (Covid) |
drinking |
Regression Discontinuity (Drinking Age) |
rd |
Generic Regression Discontinuity |
geolift1 |
GeoLift (Single cell) |
geolift_multi_cell |
GeoLift (Multi cell) |