example-datasets

# 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`.