Welcome to CausalTensor’s documentation!
CausalTensor is a Python library for doing causal inference and policy evaluation using panel data. It addresses questions like “What is the impact of strategy X to outcome Y” given time-series data colleting from multiple units. Answering such questions has wide range of applications from econometrics, operations research, business analytics, polictical science, to healthcare.
Check out the Usage section for further information.
Note
This project is under active development.
Contents
Getting Started
Tutorials
Reference
- API Reference
- Result Objects
- Difference-in-Differences (DID)
- Synthetic Difference-in-Differences (SDID)
- De-biased Convex Panel Regression (DC-PR)
- Matrix Completion with Nuclear Norm Minimisation (MC-NNM)
- Covariance PCA
- OLS Synthetic Control (SC)
- Robust Synthetic Control (RSC)
- Data Utilities
- Synthetic Data Generation
- Semi-Synthetic Experiments
- References
- Changelog