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.

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.

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

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.

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

Causal Forest DML analysis of racial approval penalties in U.S. mortgage lending | 42M HMDA applications, 2020-2024 | Working paper

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

Causal inference on 150K Yelp reviews using Double Machine Learning (EconML) to estimate the effect of star ratings, review length, and Elite badge status on useful votes — with propensity score overlap, placebo testing, and E-value sensitivity analysis.

estimate CATE and deploy uplift‑based targeting models that replace or augment traditional A/B testing.

Causal inference for promotional targeting: who should receive the email? Five CATE estimators evaluated by Qini & SNIPS policy value on Hillstrom 2008.

Causal inference project using the MineThatData E-Mail Analytics dataset. Implements logistic regression and DRLearner to estimate the causal effect of marketing emails on customer conversion. Includes CATE/ATE estimation, hypothesis testing and bootstrapping.

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 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.

Causal inference pipeline — propensity matching, doubly-robust estimation, uplift modeling, A/B test analysis with DoWhy + EconML.

End-to-end retail analytics project for demand forecasting, price elasticity estimation, feature engineering, and production-style ML orchestration.

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 inference on bank marketing data — PSM, DoWhy, and EconML Causal Forest to estimate true effect of cellular contact on subscription

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