pymatchit-causal 0.1.1

Propensity Score Matching (PSM), Coarsened Exact Matching (CEM), and Causal Inference in Python. A port of R's MatchIt.

pymatchit-causal: Propensity Score Matching in Python

Scalable Causal Inference, Propensity Score Matching (PSM), and Coarsened Exact Matching (CEM).

pymatchit-causal is a Python port of the standard R package MatchIt. It allows data scientists to preprocess data for causal inference by balancing covariates between treated and control groups using state-of-the-art matching methods.

Why use pymatchit?

If you are looking for Propensity Score Matching in Python, this library provides a robust, "R-style" workflow including:

• Propensity Score Estimation: Logistic Regression (GLM), Random Forest, GBM, Neural Networks.
• Matching Algorithms: Nearest Neighbor (Greedy), Exact, Subclassification, and Coarsened Exact Matching (CEM).
• Diagnostics: Publication-ready Love Plots (Covariate Balance), Propensity Density Plots, and ECDF plots.

Features

• Matching Methods: Nearest Neighbor, Exact, Coarsened Exact Matching (CEM), Subclassification.
• Distance Metrics: Logistic Regression (GLM), Mahalanobis, Random Forest, GBM, Neural Networks, etc.
• Diagnostics: Love Plots, ECDF Plots, Propensity Score Density Plots, and Summary Tables (SMD, Variance Ratios).
• Parity: Designed to mirror the R MatchIt API (matchit(formula, data, method=...)).

Installation

pip install pymatchit-causal

Quick Start

from pymatchit import MatchIt, load_lalonde

# 1. Load Data
df = load_lalonde()

# 2. Match (Nearest Neighbor with Caliper)
m = MatchIt(df, method='nearest', caliper=0.2)
m.fit("treat ~ age + educ + race + married + nodegree + re74 + re75")

# 3. Assess Balance
m.summary()
m.plot(type='balance')

# 4. Get Matched Data for Analysis
matched_data = m.matches()

Citation

If you use pymatchit-causal in your research, please cite it.

Until the accompanying paper is published, please cite the software directly:

Tünnermann, J. (2025). pymatchit: Propensity Score Matching and Causal Inference in Python (Version 0.1.0) [Computer software]. https://github.com/jtuenner/pymatchit

BibTeX:

@software{pymatchit-causal,
  author = {Tünnermann, Jonas},
  title = {pymatchit: Propensity Score Matching and Causal Inference in Python},
  url = {[https://github.com/jtuenner/pymatchit](https://github.com/jtuenner/pymatchit)},
  version = {0.1.0},
  year = {2025}
}