This repository contains Python code implementing causal inference techniques from Part II of the book 'Causal Inference: What If' by Hernán and Robins. It offers a direct translation of code from the book, enabling users to replicate the analyses and explore causal inference concepts using Python. The code is designed to align with the Stata, R, and SAS implementations discussed in the book.
The code provides a practical and accessible implementation of complex causal inference algorithms. It offers a clear and structured approach to applying causal inference methods. The inclusion of dependencies and data source information streamlines the setup process for users.
- Causal Inference Algorithms: Implements various causal inference methods like propensity score matching, inverse probability weighting, and instrumental variable analysis.
- Data Handling: Provides functionalities for data loading, cleaning, and preprocessing to prepare data for causal inference models.
- Visualization: Includes plotting functions to visualize causal relationships, model results, and data distributions.
- Dependencies Management: Specifies required Python packages like numpy, pandas, statsmodels, and scipy.
- Reproducibility: Facilitates reproducible research by providing a well-organized code structure and specifying data sources.
The repository is a mature project, with code aligned to the 2021 version of the book and active maintenance. It has a substantial number of stars and forks, indicating active usage. Regular updates and issue responses ensure continued reliability and relevance.
Researchers, data scientists, and students can benefit from this repository to implement causal inference techniques, analyze causal relationships in data, and reproduce findings from the 'Causal Inference: What If' book. This provides a practical alternative to manual implementations or relying solely on statistical software packages.