15 Repositories
Latest Python Libraries
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc
A Python package for causal inference using Synthetic Controls
Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim
CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.
CausalNLP CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. Install pip install -U
CausaLM: Causal Model Explanation Through Counterfactual Language Models
CausaLM: Causal Model Explanation Through Counterfactual Language Models Authors: Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart Abstract: Understan
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber
Generalized Random Forests
generalized random forests A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods fo
Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks
Obs-Causal-Q-Network AAAI 2022 - Training a Resilient Q-Network against Observational Interference Preprint | Slides | Colab Demo | Environment Setup
Dynamica causal Bayesian optimisation
Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.
pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.
pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Python-for-Epidemiologists This repository is an introduction to epidemiology analyses in Python. Additionally, the tutorials for my library zEpid are
A Python library that helps data scientists to infer causation rather than observing correlation.
A Python library that helps data scientists to infer causation rather than observing correlation.
An index of algorithms for learning causality with data
awesome-causality-algorithms An index of algorithms for learning causality with data. Please cite our survey paper if this index is helpful. @article{
Uplift modeling and causal inference with machine learning algorithms
Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
Counterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in C