{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Causallib estimates causal effects from observational data using a modular, scikit-learn-inspired API that supports pluggable machine learning models for flexible causal inference.","skillfed_tags":["causal-inference","observational-data","treatment-effects"],"use_cases":["Estimate average treatment effect (ATE) or treatment effect on the treated (ATT) from observational health or social science data.","Build conditional average treatment effect (CATE) models to predict individual-level causal effects using direct outcome methods.","Diagnose and debug causal models by applying ML evaluation metrics through a causal lens to identify model misspecification.","Chain causal inference with preprocessing pipelines to filter rows and select confounders before effect estimation.","Compare multiple causal inference strategies (IPW vs. Standardization) on the same data under a unified API."],"what_it_does":"Causallib is a Python library for estimating causal effects from observational (non-experimental) data. It provides a suite of causal inference methods\u2014weight-based models like IPW and direct outcome models like Standardization\u2014all under a unified fit-and-predict interface. You supply your own machine learning model to handle internal prediction tasks, then use causallib's methods to estimate potential outcomes and treatment effects.\n\nThe package is designed around the potential outcomes framework: it separates potential-outcome prediction from effect estimation, which allows flexible specification of stratified effects and supports multi-treatment scenarios. It includes an evaluation suite that reinterprets standard ML diagnostics from a causal perspective, helping identify when internal models underperform. The out-of-bag fit-and-predict workflow supports honest effect estimation by training on one set of examples and evaluating on another.","worth_installing":"Yes. Causallib is actively maintained, has no known vulnerabilities, and offers a well-designed, modular approach to causal inference that will appeal to researchers and practitioners working with observational data. The fit-and-predict API and support for arbitrary ML models make it flexible and integrable into existing workflows. Install it if you need to estimate treatment effects from non-experimental data."},"id":"causallib","links":{"html":"https://skillfed.io/packages/causallib","md":"https://skillfed.io/packages/causallib.md","pypi":"https://pypi.org/project/causallib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-04-06","license_spdx":null,"license_treatment":"permissive","name":"causallib","python_support":"unspecified","summary":"A Python package for flexible and modular causal inference modeling"},"popularity":{"monthly_downloads":2654498,"position":2957,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.10.0"}