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62 search results found

Dowhy ⭐ 6,730

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.

Causalml ⭐ 4,542

Uplift modeling and causal inference with machine learning algorithms

Next generation of automated data exploratory analysis and visualization platform.

Econml ⭐ 3,522

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of

Causalnex ⭐ 2,109

A Python library that helps data scientists to infer causation rather than observing correlation.

Generalized Random Forests

Causaldiscoverytoolbox ⭐ 804

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Causallib ⭐ 653

A Python package for modular causal inference analysis and model evaluations

Scikit Uplift ⭐ 572

❗ uplift modeling in scikit-learn style in python 🐍

Causal Ml ⭐ 376

Must-read papers and resources related to causal inference and machine (deep) learning

Doubleml For Py ⭐ 365

DoubleML - Double Machine Learning in Python

Causal_reading_group ⭐ 361

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Python package for causal discovery based on LiNGAM.

Auton Survival ⭐ 271

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

A resource list for causality in statistics, data science and physics

Awesome Causal Inference ⭐ 169

A (concise) curated list of awesome Causal Inference resources.

Kdd2021 Tutorial ⭐ 126

EconML/CausalML KDD 2021 Tutorial

Awesome Graph Causal Learning ⭐ 108

A list of Graph Causal Learning materials.

Doubleml For R ⭐ 88

DoubleML - Double Machine Learning in R

Perfect_match ⭐ 75

➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.

Causeinfer ⭐ 54

Machine learning based causal inference/uplift in Python

Tlverse Handbook ⭐ 51

🎯 📕 Targeted Learning in R: A Causal Data Science Handbook

Dame Flame Python Package ⭐ 48

A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

Awesome Marketing Machine Learning ⭐ 39

A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more

Awesome Decision Making Reinforcement Learning ⭐ 37

A selection of state-of-the-art research materials on decision making and motion planning.

Openasce ⭐ 35

OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.

🎯🎓 Generalized Targeted Learning Framework

💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

Rforestry ⭐ 29

Causaltoolbox ⭐ 23

Dynamic causal Bayesian optimisation

R Package: Augmented Inverse Probability Weighted (AIPW) Estimation for Average Causal Effect

Causalimages Software ⭐ 15

causalimages: An R package for performing causal inference with image and image sequence data with a JAX backend

A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects

Machine Learning Summer Schools ⭐ 14

Curated materials for different machine learning related summer schools

Cfml_tools ⭐ 12

My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context

Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)

📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling

Causalglm ⭐ 11

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

Tmle3shift ⭐ 11

🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions

Ml Notes ⭐ 11

A collection of ML notes

Lmtp Tutorial ⭐ 11

Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)

Haldensify ⭐ 11

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

Doubleml Serverless ⭐ 10

DoubleML-Serverless - Distributed Double Machine Learning with a Serverless Architecture

Medoutcon ⭐ 10

📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

A Framework for Reasoning about System Performance using Causal AI

A causal tree method for heterogeneous treatment effects in Python

Matching With Gans ⭐ 7

Matching in GAN latent space for better bias benchmarking and semantic image editing. 👶🏻🧒🏾👩🏼‍🦰👱🏽‍♂️👴🏾

Tmle3mopttx ⭐ 7

🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention

Causality4rec_paperlist ⭐ 7

This repository collects recent top papers about causal inference for recommendation. We will keep updating the paper list weekly.

Causai is a Python package for Causality in Machine Learning. We provide state-of-the-art causal algorithms and ML into decision-making systems

📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions

Collaborative Targeted Maximum Likelihood Estimation

Estimation and Inference for Context-Specific Causal Average Treatment Effect and Optimal Individualized Treatment Effect with Single Time Series

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Dml.sensemakr ⭐ 5

Sensitivity analysis tools for causal ML

Networkcausaltree ⭐ 5

Package for heterogeneous treatment and spillover effects under network interference

Causal_twitter_modeling_covid19 ⭐ 5

Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.

R package for matching algorithm in causal inference

A pure Julia implementation of the Targeted Minimum Loss-based Estimation

Graphical_model_learning ⭐ 5

Learning graphical models, with a focus on causal models and learning from interventional data.

Causal_il ⭐ 5

Contains implementation of the DoubIL and ResiduIL algorithms from the ICML '22 paper Causal Imitation Learning under Temporally Correlated Noise.

Hte Prediction Rcts ⭐ 5

Predicting treatment effects from RCTs (Circulation: CQO 2019).

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