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.
Archived (Oct 2026): rejected; misread HMDA credit-score and AUS fields, see correction notice. Superseded by hmda-racial-disparities.
Practical causal inference project evaluating the incremental impact of digital advertising using A/B testing, observational methods, Double Machine Learning, and heterogeneous treatment effects.
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).
Uplift modeling & retention API — predicting which customers a retention contact can actually save. Live on Render
Causal analysis for people who aren't data scientists: an Agent Skill for Claude
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.
Dynamic pricing via Double Machine Learning: causal uplift (EconML, DoWhy), refutation tests, risk-adjusted MILP budget allocation and a What-If Lab. Live demo on GitHub Pages.
Causal inference on 10,391 synthetic student records: regression, IPW, T-/S-Learners, Causal Forest (ATE about 0.41 to 0.42).
Codes for paper: Instrument Validity as an Audit for Algorithmic Collective Action (NeurIPS 2026 EconML)
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.
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
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