causal-forest

Generalized Random Forests

Bilibili vtuber causal inference

Practical causal inference project evaluating the incremental impact of digital advertising using A/B testing, observational methods, Double Machine Learning, and heterogeneous treatment effects.

Causal Forest DML analysis of racial approval penalties in U.S. mortgage lending | 42M HMDA applications, 2020-2024 | Under review at Journal of Financial Services Research

Análise Avançada de Intervenção em Ansiedade com Análise de Sensibilidade

Marketing Analytics project : Promotion email targeting with uplift and causal forest model

Replication files for Skinner & Doyle (2021) Do civic returns to higher education differ across subpopulations? An analysis using propensity forests

A Scala 3 ML Library for estimating causal effects

This is a project by Asmir Muminovic and Lukas Kolbe, which was created for the Applied Predictive Analytics class held by the Chair of Information Systems at the Humboldt University of Berlin

A course project for POLS 904 Statistical Computing Foundations

Repository for the Bachelor's thesis of Matej Havelka concerning the evaluation of the Causal Forests method for determining the causal effect in Machine learning.

An end-to-end causal inference project estimating who actually responds to a marketing discount, not just whether it works on average. Uses Double Machine Learning and Causal Forests (EconML) on the Starbucks promotional dataset, validated first on synthetic data with known ground truth.

OLS on observational data says job training hurts earnings. Double ML corrects the bias and recovers the $1,794 RCT ground truth. Per-individual CATE · SHAP moderators · FastAPI · Streamlit dashboard.

Uplift modelling and incentive incrementality on a simulated multi-cell fee-tier experiment: S/T/X/R meta-learners vs EconML CausalForestDML on Qini/AUUC, CUPED variance reduction, Welch/Mann-Whitney/permutation tests, volume-vs-rate revenue decomposition, and survival analysis of effect decay.

R package for causal inference with generalized random forests, including causal forest, causal survival forest, and instrumental forest workflows for heterogeneous effect estimation.

Replication and Causal ML extension of Buchmann et al. (2023) on child marriage in Bangladesh.

End-to-end uplift modeling pipeline on the Criteo dataset. Compares T/S/X-Learner and Causal Forest to estimate heterogeneous treatment effects for budget-constrained marketing targeting.

Reliable and Fair Causal Machine Learning for Sparse Subpopulations in NSDUH 2021–2023

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

Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.