Propensity Score Python Packages

morie

MORIE: multi-domain scientific computing toolkit (Python + R) for observational causal inference, survey methods, and criminal-justice analytics — home of the MRM (Multilevel Reconciliation Methodology) framework.

cbps

Python implementation of Covariate Balancing Propensity Score (CBPS) for robust causal inference in observational studies. Supports binary, multi-valued, and continuous treatments. Includes high-dimensional CBPS (hdCBPS), nonparametric CBPS (npCBPS), marginal structural models (CBMSM), and instrumental variables (CBIV).(Disclaimer: CURRENTLY WIP)

moderndid

Scalable, GPU-accelerated Python library for modern difference-in-differences.

causallift

CausalLift: Python package for Uplift Modeling for A/B testing and observational data.

moirais

Methods for Observational Inference and Robust Analysis of Interventions in Scientific Experimentation. Multi-domain scientific computing toolkit hosting the MRM framework for Canadian carceral, police, and oversight data, with general-purpose causal inference, signal processing, cryptography, spatial statistics, statistical physics, and psychometrics modules.

doublediff

Scalable, GPU-accelerated Python library for modern difference-in-differences.

causaldid

Modern difference-in-differences estimators.