Taking causal inference to the extreme!
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Updated
May 12, 2024 - Julia
Taking causal inference to the extreme!
Supplements for Blog posts
A python module for the synthetic control method
Course on Program Evaluation
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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.
[Experimental] Global causal discovery algorithms
The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.
A General Causal Inference Framework by Encoding Generative Modeling
This repository contains an R functions designed to estimate the Average Treatment Effect on the Treated (ITT) and Local Average Treatment Effect (LATE) using various methods, including Difference in Means and Difference in Differences. The function allows for adjustment for clustering and provides options for methods such as Lee Bounds and IPW
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 …
An educational Python-based introduction to causal inference techniques using machine learning.
Uplift modeling and causal inference with machine learning algorithms
causalimages: An R package for performing causal inference with image and image sequence data
Inferência Causal para os Corajosos e Verdadeiros. Uma abordagem divertida, mas rigorosa, para aprender sobre estimativa de impacto e causalidade.
A Python package for causal inference in quasi-experimental settings
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
A Python library that helps data scientists to infer causation rather than observing correlation.
Sensitivity analysis tools for causal ML
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