가짜연구소 <인과추론과 실무> 프로젝트
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Updated
May 26, 2024
가짜연구소 <인과추론과 실무> 프로젝트
Uplift modeling and causal inference with machine learning algorithms
[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.
Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
📊 🌐 🧑🏫 Website for undergraduate-level course on Educational Research and Evaluation (built with Quarto)
NORmet for Automated Air Quality Intervention Studies
Supplements for Blog posts
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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.
multimedia is an R package for multimodal mediation analysis of microbiome data. It has been designed to help integration of relative abundance, survey, and metabolomic data through causal mediation analysis.
Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner
run causal inference to detect the root causes
📝 Statistical Rethinking colearning 2024
Fast and customizable framework for automatic and quick Causal Inference in Python
A python module for the synthetic control method
Causal discovery made easy.
causalimages: An R package for performing causal inference with image and image sequence data
Code supplement for "Neuroevolutionary representations for learning heterogeneous treatment effects"
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