causalml

https://arxiv.org/abs/2009.01561

Causalis - State-of-the-art robust causal inference for experiments and observational data in python

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

KDD 2024 2nd Workshop on Causal Inference and Machine Learning in Practice

There are all learning materials that I used or assignments that I completed.

Uplift modeling & retention API — predicting which customers a retention contact can actually save. Live on Render

将五行理论与DML+集成学习融合的因果推断框架 | 稳健ATE估计 | 偏差降低89.8% | 支持银行/电商双场景

This is the replication of one R tutorial introduced in Machine Learning and Econometrics tutorial in AEA Annual Meeting 2018.

Reproducible benchmark of 7 uplift modeling approaches across 5 public datasets (Hillstrom, Criteo, Lenta, RetailHero, MegaFon) + synthetic DGP, with bootstrap CIs, robustness analysis, and presentation-ready comparison plots.

A slide deck and set of jupyter notebooks created during my time with Fellowship.ai in order to decide what method would be best for predicting churn using the most up to date and innovative methods.

Exploratory project using uber's causalml to estimate average treatment effects on IT incident durations.

A single behavioral nudge nearly 5×'d email click-through in a randomized experiment. This project proves the effect causally, scales it to ~200K extra clicks, and uses Causal ML to pinpoint exactly which customers to target.

Comparison of 6 uplift-modeling approaches on the Lenta dataset

Uplift-модель для таргетированного маркетинга (промокоды). T-learner, R-learner, Optuna, MLflow, Uplift@30%.

Reproducible benchmark of uplift-modeling approaches (meta-learners, causal forests, DML, IV) on the Criteo Uplift dataset.

Causal uplift modeling (X-Learner) on a 3-arm email A/B test, identifies persuadable customers and cuts targeting volume 50% while capturing 61-72% of incremental conversions.

Replicates Nigeria egg demand-creation study; adds targeting & media-mix optimization.

Business strategy simulation dashboard using A/B testing and Causal Inference