{"repo":"andrewtavis/causeinfer","free":true,"listed":false,"github":"https://github.com/andrewtavis/causeinfer","clone":"git clone https://github.com/andrewtavis/causeinfer.git","description":"Machine learning based causal inference/uplift in Python","language":"Python","stars":63,"topics":["causal-inference","machine-learning","treatment-effects","uplift-modeling","causality","data-science","statistics","econometrics","python","uplift"],"license":"BSD-3-Clause","category":"machine-learning","readme_excerpt":"Machine learning based causal inference/uplift in Python causeinfer is a Python package for estimating average and conditional average treatment effects using machine learning. The goal is to compile causal inference models both standard and advanced, as well as demonstrate their usage and efficacy - all this with the overarching ambition to help people learn causal inference techniques across business, medical, and socioeconomic fields. See the documentation for a full outline of the package including the available models and datasets. Contents - Installation - Application - Two Model Approach - Interaction Term Approach - Class Transformation Approaches - Reflective and Pessimistic Uplift - Evaluation Methods - Visualization - Model Iteration - Data and Examples - Business Analytics - Medical Trials - Socioeconomic Analysis - Development environment - To-Do - References Installation causeinfer is available for installation via uv (recommended) or pip. For Users For Development Build Back to top. Application Standard Algorithms Two Model Approach Separate models for treatment and control groups are trained and combined to derive average treatment effects (Hansotia, 2002). Interaction Term Approach An interaction term between treatment and covariates is added to the data to allow for a basic single model application (Lo, 2002). Class Transformation Approaches Units are categorized into two or four classes to derive treatment effects from favorable class attributes (Lai, 2006;","default_branch":null,"files":null,"tree":[],"storefront":"/r/andrewtavis","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/andrewtavis/causeinfer/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}