[TNNLS-2024, arXiv-2023.2.10] Official repository of "A Survey on Causal Reinforcement Learning"
-
Updated
Apr 19, 2026
#
casual-inference
Here are 28 public repositories matching this topic...
Articles/ Journals and Videos related to Economics:chart_with_upwards_trend: and Data Science :bar_chart:
-
Updated
Jun 9, 2022 - HTML
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
-
Updated
Dec 27, 2023 - Jupyter Notebook
Just to keep track of nice blog posts and new announcements related to machine learning, deep learning and artificial intelligence
machine-learning deep-learning neural-network machine-learning-algorithms deep-reinforcement-learning artificial-intelligence dataset deep-learning-algorithms artificial-intelligence-algorithms casual-inference
-
Updated
Feb 3, 2019
Clinical-AI Research Framework
machine-learning clinical-research interdisciplinary casual-inference cohort-studies shap case-control-studies
-
Updated
Jun 17, 2022 - Jupyter Notebook
python data-science machine-learning time-series scikit-learn forecasting uplift-modeling mlops casual-inference
-
Updated
Apr 5, 2026 - Jupyter Notebook
Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling
inference gibbs-sampling-algorithm casual-inference bayesian-belief-networks approximate-inference-algorithm probability-propagation
-
Updated
Jul 15, 2020 - Jupyter Notebook
Important link of cancer epidemiology and cancer prevention.
-
Updated
Jul 4, 2022 - Jupyter Notebook
🧠 Implement a bi-factual contrastive explanation system for AI decisions, enhancing understanding through formal definitions and optimized algorithms.
causal-models explainable-ai xai casual-inference counterfactual-explanations contrastive-explanations causal-graphs causal-graphical-models causal-graph counterfactual-contrastive-learning
-
Updated
Apr 23, 2026 - Jupyter Notebook
Predicting Recession in Economy using various macro economic indicators
-
Updated
Mar 5, 2026 - Jupyter Notebook
The Impact of Uber on Taxi Rides: A Causal Inference Study
-
Updated
Aug 19, 2024 - Python
pip install gptmed
redis deep-learning pytorch pip deep-learning-algorithms gpt language-model agents casual-inference conversation-ai transformer-architecture llm medical-llm tiny-llm question-answering-model model-training-and-optimization agentic-ai medical-question-answering-llm tiny-language-model
-
Updated
Mar 22, 2026 - Python
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).
-
Updated
Aug 12, 2024 - C
Implémentation d’un système d’IA Explicable (XAI) basé sur les explications contrastives bi-factuelles, avec optimisations algorithmiques et interface graphique CausaLytics.
causal-models explainable-ai xai casual-inference counterfactual-explanations contrastive-explanations causal-graphs causal-graphical-models causal-graph counterfactual-contrastive-learning
-
Updated
Aug 30, 2025 - Jupyter Notebook
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
python athena aws-s3 pyspark scipy anomaly-detection rfm casual-inference fastapi funnel-analysis behavioral-analytics
-
Updated
Mar 31, 2026 - Python
U.S. Mental Health Demand–Supply Gap Monitor (Google Trends + HRSA data) with interpretable ML (XGBoost/SHAP)
python data-science time-series data-visualization xgboost forecasting feature-engineering publichealth google-trends xgboost-regression casual-inference shap
-
Updated
Feb 1, 2026 - Python
An explainable data science system that detects demand anomalies and attributes their root causes using time-series analysis and machine learning.
data-science machine-learning-algorithms supply-chain demand-forecasting time-series-analysis anomaly-detection explainable-ai casual-inference root-cause-analysis
-
Updated
Jan 9, 2026 - Jupyter Notebook
Causal inference can be defined as the process by which causes are inferred from the data. In this project, data from breast cancer diagnosis is analyzed and causes inferred from this analysis.
-
Updated
Aug 29, 2021 - Jupyter Notebook
Improve this page
Add a description, image, and links to the casual-inference topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the casual-inference topic, visit your repo's landing page and select "manage topics."