{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Causalml estimates the causal impact of treatments on outcomes at the individual level using machine learning, providing methods to compute Conditional Average Treatment Effect (CATE) from experimental or observational data.","skillfed_tags":["causal-inference","treatment-effect-estimation","uplift-modeling"],"use_cases":["Identify which customers will respond favorably to an advertising campaign by estimating individual-level treatment effects from A/B test data.","Recommend personalized product offerings or messaging channels by estimating heterogeneous effects for each customer-treatment combination.","Analyze observational data to estimate causal impact when randomized experiments are infeasible or unethical.","Build targeting policies that maximize ROI by selecting customers with the highest predicted uplift.","Feature selection for uplift modeling to identify which customer attributes drive treatment response variation."],"what_it_does":"Causalml is a Python package for estimating heterogeneous treatment effects and performing causal inference using machine learning. It provides a standard interface to compute the Conditional Average Treatment Effect (CATE)\u2014the causal impact of an intervention on an outcome for individual users\u2014from A/B experiments or observational data, without requiring strong assumptions about model form.\n\nThe package implements multiple causal inference methods based on recent research, including metalearners, doubly robust estimation, and tree-based approaches. It's designed for practical applications like campaign targeting (identifying customers most likely to respond to an ad) and personalized engagement (recommending optimal treatments for each customer). With 18 runtime dependencies spanning xgboost, lightgbm, statsmodels, and visualization libraries, it trades installation weight for a comprehensive toolkit.","worth_installing":"Yes, if you need to estimate causal effects or uplift at the individual level. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and provides a suite of methods backed by published research. The medium install friction (18 dependencies) is justified by the breadth of algorithms and visualization tools. Not suitable if you need minimal dependencies or are restricted to Python versions below 3.11."},"id":"causalml","links":{"html":"https://skillfed.io/packages/causalml","md":"https://skillfed.io/packages/causalml.md","pypi":"https://pypi.org/project/causalml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-04","license_spdx":null,"license_treatment":"permissive","name":"causalml","python_support":"supports_current","summary":"Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms"},"popularity":{"monthly_downloads":144829,"position":11141,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.17.0"}