DowhyDoWhy 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.
Stars: β 3,480 (+6860%)
Mutual labels: causal-inference, causal-machine-learning
pygnaA Python package for gene network analysis
Stars: β 25 (-50%)
Mutual labels: biostatistics
doubleml-for-pyDoubleML - Double Machine Learning in Python
Stars: β 129 (+158%)
Mutual labels: causal-inference
causaldagPython package for the creation, manipulation, and learning of Causal DAGs
Stars: β 82 (+64%)
Mutual labels: causal-inference
doubleml-for-rDoubleML - Double Machine Learning in R
Stars: β 58 (+16%)
Mutual labels: causal-inference
PgmpyPython Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.
Stars: β 1,942 (+3784%)
Mutual labels: causal-inference
Awesome-Neural-LogicAwesome Neural Logic and Causality: MLN, NLRL, NLM, etc. ε ζζ¨ζοΌη₯η»ι»θΎοΌεΌΊδΊΊε·₯ζΊθ½ι»θΎζ¨ηεζ²Ώι’εγ
Stars: β 106 (+112%)
Mutual labels: causal-inference
TrendinessOfTrendsThe Trendiness of Trends
Stars: β 14 (-72%)
Mutual labels: biostatistics
stremrStreamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
Stars: β 33 (-34%)
Mutual labels: targeted-learning
cobaltCovariate Balance Tables and Plots - An R package for assessing covariate balance
Stars: β 52 (+4%)
Mutual labels: causal-inference
Python-for-EpidemiologistsTutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Stars: β 107 (+114%)
Mutual labels: causal-inference
causal-learnCausal Discovery for Python. Translation and extension of the Tetrad Java code.
Stars: β 428 (+756%)
Mutual labels: causal-inference
CausalmlUplift modeling and causal inference with machine learning algorithms
Stars: β 2,499 (+4898%)
Mutual labels: causal-inference
drnetππ 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.
Stars: β 48 (-4%)
Mutual labels: causal-inference
joineRMLR package for fitting joint models to time-to-event data and multivariate longitudinal data
Stars: β 24 (-52%)
Mutual labels: biostatistics
Causal Reading GroupWe 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.
Stars: β 339 (+578%)
Mutual labels: causal-inference
CIKM18-LCVACode for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
Stars: β 13 (-74%)
Mutual labels: causal-inference
cfml toolsMy 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
Stars: β 24 (-52%)
Mutual labels: causal-inference
rsimsumAnalysis of simulation studies including Monte Carlo error
Stars: β 19 (-62%)
Mutual labels: biostatistics
CozCoz: Causal Profiling
Stars: β 2,719 (+5338%)
Mutual labels: causal-inference
