bradyneal / causal-inference-booksLinks
Flowchart to help choose which causal inference book to read. See https://bradyneal.github.io/which-causal-inference-book for more info such as mini reviews of some of the books in the flowchart.
☆60Updated 5 years ago
Alternatives and similar repositories for causal-inference-books
Users that are interested in causal-inference-books are comparing it to the libraries listed below
Sorting:
- ☆81Updated 5 years ago
- Resources related to causality☆267Updated last year
- Seminar on Limitations of Interpretable Machine Learning Methods☆57Updated 5 years ago
- ☆94Updated 3 years ago
- Materials for class on topics in deep learning (STAT 991, UPenn/Wharton)☆95Updated 2 years ago
- 💊 Comparing causality methods in a fair and just way.☆140Updated 5 years ago
- Some notes on Causal Inference, with examples in python☆154Updated 5 years ago
- Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction☆158Updated 4 years ago
- Causal Inference & Deep Learning, MIT IAP 2018☆89Updated 7 years ago
- GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference☆83Updated 9 months ago
- Statistical Rethinking with PyTorch and Pyro☆164Updated 5 months ago
- A (concise) curated list of awesome Causal Inference resources.☆243Updated 3 years ago
- Materials of the Nordic Probabilistic AI School 2019.☆129Updated 5 years ago
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆57Updated 2 years ago
- Bayesian Bandits☆68Updated 2 years ago
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data☆63Updated 2 months ago
- Causing: CAUsal INterpretation using Graphs☆58Updated 2 weeks ago
- python app for doing personalized causal medicine using the methods invented by Judea Pearl et al.☆25Updated 2 years ago
- EconML/CausalML KDD 2021 Tutorial☆163Updated 2 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Introductory overview of Bayesian inference☆43Updated 6 years ago
- ☆30Updated 6 years ago
- Model Agnostic Counterfactual Explanations☆88Updated 3 years ago
- Resources to learn more about Machine Learning and Artificial Intelligence☆27Updated 4 years ago
- A curated list of awesome work on causal inference, particularly in machine learning.☆108Updated 4 years ago
- Code used in the causality course (401-4632-15) at ETH Zurich.☆23Updated 6 years ago
- Causal Graphical Models in Python☆248Updated 2 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆84Updated 7 years ago
- Multi-Objective Counterfactuals☆42Updated 3 years ago
- Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference☆27Updated 5 years ago