example-datasets

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

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.