econml

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.

Experimental causal-inference research on financial regimes: PCMCI+, ICP and causal forests over market data (work in progress)

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

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.

Causal inference dashboard — estimating price elasticity of customer engagement on 258K Amazon products using Double ML + Causal Forest"

Causal ML pipeline for e-commerce dynamic pricing — Double Machine Learning for unbiased price elasticity, LightGBM demand forecasting (MAPE=0.418, R²=0.055), and a FastAPI pricing service delivering +30% revenue lift across 49,677 SKUs from 32M+ transactions.

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

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

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

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

A modular Python benchmark for uplift modeling on the Criteo dataset, comparing S-Learner, T-Learner, X-Learner, DR-Learner, Causal Forest, and response-model targeting policies.

Estimates whether an intervention actually caused an outcome, from observational data: propensity matching, IPW, S/T/X-learners, DiD and IV. Then tries to break its own result with refutation tests and an E-value — and reports "no effect" when that is the honest answer.

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

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.

Economic analysis of algorithmic monoculture in machine learning-based site-specific fertilizer recommendation using Fertimap data and Turba Open Lab models.

End-to-end causal inference study estimating the effect of smoking cessation on substantial weight gain using propensity methods, DoWhy, and doubly robust EconML estimators.

From churn prediction to causal retention decisions

Causal inference analysis of ICU beta-blocker treatment effects using propensity matching, IPW, doubly robust estimation, Double ML, and Causal Forest on eICU data

Análise para responder se a hora extra realmente causa saída de funcionários ou se outros fatores como cargo e salário explicam essa relação. Usando três métodos independentes de estimação causal, o efeito direto da hora extra foi de +21,1% na rotatividade — confirmado em testes de robustez.

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.