[TNNLS-2024, arXiv-2023.2.10] Official repository of "A Survey on Causal Reinforcement Learning"
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Updated
Jun 8, 2026
[TNNLS-2024, arXiv-2023.2.10] Official repository of "A Survey on Causal Reinforcement Learning"
Articles/ Journals and Videos related to Economics:chart_with_upwards_trend: and Data Science :bar_chart:
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
Just to keep track of nice blog posts and new announcements related to machine learning, deep learning and artificial intelligence
Causal uplift modeling system for customer targeting and marketing ROI optimization
Clinical-AI Research Framework
Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling
Important link of cancer epidemiology and cancer prevention.
Predicting Recession in Economy using various macro economic indicators
The Impact of Uber on Taxi Rides: A Causal Inference Study
Statistical validity engine that audits A/B experiments across 8 checks:SRM, power, peeking, SUTVA, and more.
pip install gptmed
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 reasoning middleware for LLMs — catches false causal claims in AI outputs
8-week Data Science Mock Internship on Optimizing Recommendation Algorithm of Wayfair E-commerce
Implémentation d’un système d’IA Explicable (XAI) basé sur les explications contrastives bi-factuelles, avec optimisations algorithmiques et interface graphique CausaLytics.
401k_verification, the C program simulates and analyses a 401K dataset, implementing the B-Learner method to estimate bounds for the Conditional Average Treatment Effect (CATE).
Uplift modeling: deciding who to target with a marketing campaign to maximize incremental revenue; not just who buys. Experiment design, S/T/X-learners, Qini evaluation, and a cost aware targeting policy with a Streamlit app.
Large-scale behavioral analytics pipeline — PySpark ETL on 10M+ e-commerce events, statistical anomaly detection (Z-Score/IQR/Isolation Forest), Granger causality root cause analysis, conversion funnel + RFM segmentation, AWS S3 + Athena SQL layer (moto mock), FastAPI dashboard
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