matthewvowels1 / Awesome-Causal-InferenceLinks
A curated list of awesome work on causal inference, particularly in machine learning.
☆108Updated 4 years ago
Alternatives and similar repositories for Awesome-Causal-Inference
Users that are interested in Awesome-Causal-Inference are comparing it to the libraries listed below
Sorting:
- Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflo…☆337Updated 10 months ago
- A data index for learning causality.☆476Updated last year
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆77Updated 4 years ago
- A (concise) curated list of awesome Causal Inference resources.☆243Updated 3 years ago
- Resources related to causality☆267Updated last year
- EconML/CausalML KDD 2021 Tutorial☆162Updated 2 years ago
- Example causal datasets with consistent formatting and ground truth☆89Updated 5 months ago
- Must-read papers and resources related to causal inference and machine (deep) learning☆738Updated 2 years ago
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆31Updated 5 years ago
- CSuite: A Suite of Benchmark Datasets for Causality☆75Updated 2 years ago
- Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction☆157Updated 4 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
- Causal discovery algorithms and tools for implementing new ones☆226Updated 2 months ago
- Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.☆147Updated last year
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)☆62Updated 4 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆130Updated 2 years ago
- 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…☆91Updated 2 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.☆75Updated 4 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- ☆189Updated 2 years ago
- Non-parametrics for Causal Inference☆49Updated 3 years ago
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018☆62Updated 5 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆60Updated last year
- Scalable open-source software to run, develop, and benchmark causal discovery algorithms☆72Updated this week
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆57Updated 2 years ago
- ☆40Updated 5 years ago
- Causal Discovery from Nonstationary/Heterogeneous Data.☆53Updated 5 years ago
- Short tutorials on the use of machine learning methods for causal inference☆48Updated 2 years ago
- ☆27Updated 3 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆25Updated 2 years ago