ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

EconML/CausalML KDD 2021 Tutorial

Neuro-Symbolic-Causal AI - Project Chimera | 🌌 An open research project exploring formal verification of AI agent decisions, combining symbolic reasoning, causal inference, and runtime policy enforcement.

ODSC 2023 workshop materials on causal graphs using implementations of DoWhy (PyWhy, EconML)

How to use EconML within R

Tutorial for end-to-end causal analysis from causal discovery to causal inference. causica, lingam and econml are the main libraries used for the tutorial.

Code Repository for "Causal Inference and Discovery in Python. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more"

DeepEcon: Your one-stop Python package for econometric algorithms.

Cusal Inference applied to timeseries, uses an event database to generate a timeseries of the outcome given a sliding window containing events. Useful to add causal outcomes of events into multivariate timeseries forecasting models.

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DoWhy/EconML toolkit for visualizing causal paths and estimating treatment effects

A complete end-to-end AI experimentation & causal inference project using A/B testing, X-Learner, CATE estimation, and uplift segmentation on 1.5M+ synthetic SaaS behavioral records. Includes statistical analysis, causal ML workflow, uplift modeling, feature importance, and business-ready insights for AI feature rollout & monetization.

Impact of Referral Program on Revenue | Python, CausalML, DoWhy, EconML, Jupyter Notebook: Applied S-, T-, & X-learner causal models to quantify the referral program's impact, revealing a +$59.74 increase in order value (p < 0.001) and significantly boosting e-commerce revenue

AI-driven dynamic pricing engine using Polynomial Regression to model Price Elasticity of Demand and maximize revenue based on data-driven insights.

A Jupyter notebook demonstrating doubleML

This repository serves as my long-term, continuously maintained research and engineering space for financial time-series modeling.

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Causal analysis to predict factors affecting employee resignation using the Causal Inference method on company datasets.

Internship Project on Causal Inference (The causal effect of multi-level treatment of intervention using observational data).

Open-source causal-multimodal engine for creative attribution. Answers why a creative works — not just which one performed better — using Gemini Embedding 2, DoWhy, and EconML.

A conda-smithy repository for econml.

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Uplift Modelling - Causal ML / EconML