causal-forest

Generalized Random Forests

Bilibili vtuber causal inference

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

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

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

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.

Análise Avançada de Intervenção em Ansiedade com Análise de Sensibilidade

R

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.

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

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

Deep-dive course on causal inference for platform settings: modern DiD, synthetic control, causal forests, policy learning, and matrix completion. 8 modules of concepts, xaringan slides, and hand-coded R exercises.

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

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

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.

Causal ML for Orange Juice Price Elasticity

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.

This is a repository of the master thesis on Casual Machine Learning for Heterogeneous Treatment Effects: An Empirical Application on Optimal Treatment Assignment.