psmpy

library0.3.16pypypi✓ verified 82d ago

psmpy provides propensity score matching for observational studies, including functions to compute propensity scores, perform matching (with/without replacement, caliper), and generate graphical plots (balancing, histogram). Version 0.3.16 is the latest release. Release cadence is low; last update was 2024.

data ai-ml

pip install psmpy

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · PSMPY

psmpy

datapythonv0.3.16

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

PsmPy

✓ from psmpy import PsmPy

✗ from psmpy import Psmpy

Basic workflow: initialize with data, compute scores, match, and inspect matches.

import pandas as pd from psmpy import Psmpy from sklearn.linear_model import LogisticRegression # Sample data df = pd.DataFrame({ 'treatment': [0, 1, 0, 1, 0, 1], 'age': [30, 40, 35, 45, 25, 50], 'income': [50000, 60000, 55000, 65000, 45000, 70000] }) # Initialize Psmpy psm = Psmpy(data=df, treatment='treatment', indx='age', exclude_cols=['income'], logistic_model=LogisticRegression(solver='liblinear')) # Compute propensity scores psm.pscore() # Perform matching psm.match(method='nearest', caliper=None, replace=False) # View matched pairs print(psm.matched_ids)

Debug

Known issues

breakingIn version 0.3.15 and earlier, the 'indx' parameter was called 'idcol'. It was renamed in 0.3.16. Using 'idcol' raises TypeError.

fix

Use 'indx' instead of 'idcol' when initializing Psmpy.

affects: >=0.3.16

gotchaThe 'logistic_model' parameter expects a scikit-learn LogisticRegression instance with solver='liblinear' (or similar) that supports predict_proba. Using default solver may raise warnings or errors on small datasets.

fix

Explicitly instantiate LogisticRegression(solver='liblinear').

affects: all

gotchaThe 'exclude_cols' parameter should include any columns not used for propensity score estimation (e.g., outcome variables, IDs). Forgetting to exclude the treatment column can cause errors.

fix

List all columns that are not predictors in 'exclude_cols'.

affects: all

Upgrade

Version history

0.3.16latest on PyPI · released Nov 12, 2025

Audit

Dependencies

pandasrequiredData handling for propensity score computation and matching

numpyrequiredNumerical operations

scikit-learnrequiredLogistic regression for propensity score estimation

matplotlibrequiredPlotting balance and histogram graphs

Agent activity

7 hits · last 30 days

node

Resources

githubhttps://github.com/adriennekline/psmpy ↗

packagehttps://pypi.org/project/psmpy/ ↗

docshttps://pypi.org/project/psmpy ↗