dowhy
Causal analysis tool
A Python library for explicitly modeling and testing causal relationships in data.
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.
7k stars
140 watching
937 forks
Language: Python
last commit: 4 months ago bayesian-networkscausal-inferencecausal-machine-learningcausal-modelscausalitydata-sciencedo-calculusgraphical-modelsmachine-learningpython3treatment-effects
Related projects:
Repository | Description | Stars |
---|---|---|
| Provides statistical methods to estimate the impact of interventions on outcomes in machine learning models | 5,132 |
| Developing regression models to predict greenhouse gas emissions from building energy usage using Google Cloud and machine learning techniques | 19 |
| Displays historical star counts for GitHub repositories in a graphical format | 6,738 |
| Builds fast and interactive data visualizations as code using markdown and SQL | 4,632 |
| A unified framework for evaluating large language models' performance and robustness in various scenarios. | 2,487 |
| A platform for curating and sharing knowledge among technical professionals using data formats and tools commonly used in these fields. | 5,489 |
| Improves the performance of large language models by intervening in their internal workings to reduce hallucinations | 83 |
| An R implementation of a machine learning approach to estimate the effect of treatment on individuals | 5 |
| An online annotation platform providing an API for web-based annotators. | 2,972 |
| A framework for evaluating large language models and systems, providing a registry of benchmarks. | 15,168 |
| A hotel management and reservation system that enables online booking and central management of properties. | 4,593 |
| A Python scikit for building and analyzing recommender systems using explicit rating data | 6,434 |
| A library that enables data scientists and machine learning engineers to log and analyze their datasets in real-time, providing insights into data quality and model performance. | 2,664 |
| Generates high-density visualizations to kickstart Exploratory Data Analysis with two lines of code. | 2,965 |
| A repository providing code and models for research into language modeling and multitask learning | 22,644 |