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.
76
95%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
CausalPy ships with built-in datasets that can be loaded with cp.load_data(...).
import causalpy as cp
df = cp.load_data("did")| 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 |
choosing-causalpy-methods after identifying the data shape.running-causalpy-experiments.7e23946
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.