doubleml

Automated Double Machine Learning (DoubleML + AutoML) pipeline for causal inference — estimate incremental impact, CATE & ROI of promotions, pricing and media.

Repository for DoubleML tutorial at UseR!2022, UAI2022 and Tools for Causality 2023

Replication of Simulations in Bach et al. (2024) - DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, https://doi.org/10.18637/jss.v108.i03

Shiny App illustrating the Key Ingredients of the Double Machine Learning Approach

This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.

minimal, fast, object-oriented implementation of the AIPW and related estimators for many discrete treatments. Implemented with scikitlearners and cross-fitting.

HSE FES Econometrics-2 (advanced course) project

面板数据双重机器学习双重差分（DML-DID） | Config-driven Python pipeline for double/debiased machine learning DID on firm-year panels with two-way fixed effects: 5 ML learners vs. TWFE OLS, applied to 4 policy evaluations

Données simulées pour montrer qu'un modèle peut bien prédire sans mesurer le bon effet causal : régression fallacieuse, confondeur, OLS et Double ML.

Single Stock predictions using various machine learning methods

Causal inference for insurance pricing. Double Machine Learning with CatBoost nuisance models, confounding bias reports, Poisson/Gamma/binary outcomes.

Causal inference on a retention campaign. 19.3% of the customer base was actively harmed by the same intervention that helped everyone else.

Double Machine Learning estimate of price elasticity from observational retail data, served via Streamlit.

Double Machine Learning analysis of the causal effect of public health financing on out-of-pocket expenditure using WHO GHED data

various causal modeling techniques to determine if living in the U.S. or Europe impacts developers’ overall job satisfaction.

Causal effect of hybrid vs gasoline powertrain on fuel consumption (EPA data, 9 estimators + robustness suite)