{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"PsmPy implements propensity score matching for observational studies, using logistic regression and KNN algorithms to match treated and control subjects on covariates and estimate causal effects.","skillfed_tags":["causal-inference","epidemiology","observational-studies"],"use_cases":["Estimate treatment effects in retrospective cohort studies where randomization is infeasible or unethical","Assess whether an intervention (e.g., a medical procedure) causally affects outcomes (e.g., mortality, length of stay) in observational data","Balance covariates between treated and control groups to reduce confounding in epidemiological analyses","Visualize covariate imbalance before and after matching to validate the matching procedure","Compare multiple matching strategies (1:1 vs. 1:many, with/without replacement) on the same dataset"],"what_it_does":"PsmPy is a Python library for propensity score matching, a statistical technique used in observational studies to estimate causal effects by matching treated and control subjects on their propensity scores\u2014the predicted probability of receiving treatment given observed covariates. It reduces confounding bias by creating comparable treatment and control groups, mimicking the balance achieved in randomized trials.\n\nThe package provides logistic regression to compute propensity scores, KNN-based matching algorithms supporting both 1:1 and 1:many matching with optional caliper constraints, and visualization tools to assess covariate balance before and after matching. It integrates with pandas, scikit-learn, and matplotlib, and includes effect size calculations (Cohen's D) to quantify the standardized mean differences between groups.","worth_installing":"Yes, if you are conducting observational studies in epidemiology or health research and need propensity score matching. The package is straightforward to use, well-integrated with the scientific Python stack, and permissively licensed. However, maintenance is aging (last release 275 days ago), so consider it stable for established workflows but not actively developed; for active support or cutting-edge features, evaluate alternatives or contribute to the repository."},"id":"psmpy","links":{"html":"https://skillfed.io/packages/psmpy","md":"https://skillfed.io/packages/psmpy.md","pypi":"https://pypi.org/project/psmpy/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-11-12","license_spdx":null,"license_treatment":"permissive","name":"psmpy","python_support":"unspecified","summary":"Propensity score matching for python and graphical plots"},"popularity":{"monthly_downloads":2663512,"position":2952,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.3.16"}