MLResearchAtOSRAM / cause2eLinks
The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel GrΓΌnbaum (@dg46).
β58Updated last month
Alternatives and similar repositories for cause2e
Users that are interested in cause2e are comparing it to the libraries listed below
Sorting:
- π Comparing causality methods in a fair and just way.β139Updated 5 years ago
- GRAPL: A computational library for nonparametric structural causal modelling, analysis and inferenceβ81Updated 6 months ago
- [Experimental] Global causal discovery algorithmsβ103Updated this week
- Python implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakrβ51Updated last month
- Example causal datasets with consistent formatting and ground truthβ84Updated 2 months ago
- This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop aβ¦β61Updated 10 months ago
- [ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"β44Updated last year
- Makes algorithms/code in Tetrad available in Python via JPypeβ79Updated this week
- Notes, exercises and other materials related to causal inference, causal discovery and causal ML.β140Updated 10 months ago
- Ananke named for the Greek primordial goddess of necessity and causality, is a python package for causal inference using the language oβ¦β14Updated 5 years ago
- [Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.β56Updated this week
- AutoML for causal inference.β223Updated 6 months ago
- Active Bayesian Causal Inference (Neurips'22)β56Updated 10 months ago
- Fit Sparse Synthetic Control Models in Pythonβ83Updated last year
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring codeβ83Updated 7 years ago
- List of python packages for causal inferenceβ17Updated 3 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causβ¦β77Updated 4 years ago
- A framework for generating complex and realistic datasets for use in evaluating causal inference methods.β31Updated 3 years ago
- Multi-Objective Counterfactualsβ41Updated 2 years ago
- A full example for causal inference on real-world retail data, for elasticity estimationβ50Updated 3 years ago
- A curated list of awesome work on causal inference, particularly in machine learning.β105Updated 4 years ago
- Causal Graphical Models in Pythonβ245Updated 2 years ago
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical dataβ58Updated last year
- A unified interface for the estimation of causal networksβ22Updated 5 years ago
- Some notes on Causal Inference, with examples in pythonβ153Updated 5 years ago
- An open-source package of causal feature selection and causal (Bayesian network) structure learning (C/C++ version)β60Updated 4 years ago
- Scalable open-source software to run, develop, and benchmark causal discovery algorithmsβ71Updated this week
- Python package for (conditional) independence testing and statistical functions related to causality.β28Updated 2 weeks ago
- Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.β142Updated last year
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)β52Updated 4 years ago