CausalPy is a Python package used for causal inference in quasi-experimental settings, often utilizing Bayesian inference for robust analysis.

What types of datasets are available with this skill?

It includes specialized datasets for Difference-in-Differences, Interrupted Time Series, Synthetic Control, and Regression Discontinuity designs.

Can I use this skill to load my own private CSV files?

This specific skill is designed for the CausalPy example library; for private data, you should use standard data loading methods like pandas.read_csv().

Do I need to provide external files to use these examples?

No, this skill loads internal datasets pre-packaged with the CausalPy library, making it ideal for immediate demos and testing.

CausalPy Dataset Loader

Name: CausalPy Dataset Loader
Author: pymc-labs

bypymc-labs

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1,080

Ciencia de Datos y ML

Accesses built-in example datasets for causal inference and quasi-experimental modeling.

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This skill enables Claude to quickly load and explore internal CausalPy example datasets, facilitating immediate demonstrations and testing of causal inference methods. It supports a wide range of experimental designs, including Difference-in-Differences (DiD), Interrupted Time Series (ITS), Synthetic Control (SC), and Regression Discontinuity (RD). By providing instant access to curated data like the Brexit impact study or bank-specific DiD examples, it allows users to jump directly into analysis and model validation without needing to source external data files.

Características Principales

Loads real-world example data including Brexit and Covid impact datasets
Streamlines the learning process for Bayesian causal inference
Accesses standard quasi-experimental datasets (DiD, ITS, SC, RD)
Simplifies the setup of CausalPy model demonstrations
1,080 GitHub stars
Provides generic data templates for various statistical designs

Casos de Uso

Demonstrating a Difference-in-Differences (DiD) analysis using the banks dataset
Testing a Synthetic Control model using the Brexit example data
Learning how to implement Interrupted Time Series (ITS) with Covid-related data

Acerca de

Características Principales

Loads real-world example data including Brexit and Covid impact datasets
Streamlines the learning process for Bayesian causal inference
Accesses standard quasi-experimental datasets (DiD, ITS, SC, RD)
Simplifies the setup of CausalPy model demonstrations
1,080 GitHub stars
Provides generic data templates for various statistical designs

Casos de Uso

Demonstrating a Difference-in-Differences (DiD) analysis using the banks dataset
Testing a Synthetic Control model using the Brexit example data
Learning how to implement Interrupted Time Series (ITS) with Covid-related data