jointVIP
jointVIP: Prioritizing variables in observational study design with joint variable importance plot in R - Published in JOSS (2024)
policytree
policytree: Policy learning via doubly robust empirical welfare maximization over trees - Published in JOSS (2020)
txshift
txshift: Efficient estimation of the causal effects of stochastic interventions in R - Published in JOSS (2020)
haldensify
haldensify: Highly adaptive lasso conditional density estimation in R - Published in JOSS (2022)
CVtreeMLE
CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R - Published in JOSS (2023)
medoutcon
medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R - Published in JOSS (2022)
biotmle
biotmle: Targeted Learning for Biomarker Discovery - Published in JOSS (2017)
CausalInference
Causal inference, graphical models and structure learning in Julia
treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
pgmpy
Python library for building, learning, and reasoning with causal models.
nl-causal-representations
This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).
https://github.com/py-why/dowhy
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.
causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
causalegm
A General Causal Inference Framework by Encoding Generative Modeling
https://github.com/biomedsciai/causallib
A Python package for modular causal inference analysis and model evaluations
https://github.com/bytedance/causalmatch
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
environmental-impact-assessment
performs Bayesian inference modeling to scope environmental impact assessments in sociotechnical energy systems
cobalt
Covariate Balance Tables and Plots - An R package for assessing covariate balance
rdrobust
Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
regmedint
R implementation of effect measure modification-extended regression-based closed-formula causal mediation analysis
lmtp
:package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
AIPW
R Package: Augmented Inverse Probability Weighted (AIPW) Estimation for Average Causal Effect
VLTimeCausality
A framework to infer causality on a pair of time series of real numbers based on Variable-lag Granger causality and transfer entropy.
https://github.com/juliadynamics/associations.jl
Algorithms for quantifying associations, independence testing and causal inference from data.
https://github.com/andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python
https://github.com/cran-task-views/causalinference
CRAN Task View: Causal Inference
https://github.com/csinva/matching-with-gans
Matching in GAN latent space for better bias benchmarking and semantic image editing. 👶🏻🧒🏾👩🏼🦰👱🏽♂️👴🏾
multimedia
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.
jointcalib
Repository for a small package for joint calibration of totals, quantiles and other metrics
ATbounds
Bounding Treatment Effects by Pooling Limited Information across Observations
drtmle
Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests
https://github.com/alan-turing-institute/causal-cyber-defence
This repository contains glue-code necessary to run dynamic Causal Bayesian optimisation within the Yawning Titan cyber-simulation environment.
TMLE.jl
TMLE.jl: Targeted Minimum Loss-Based Estimation in Julia - Published in JOSS (2025)
bcf-iv
Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)
https://github.com/benlansdell/rdd
python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper
https://github.com/aiandglobaldevelopmentlab/causalimages-software
causalimages: An R package for performing causal inference with image and image sequence data
medRCT
medRCT: Causal mediation analysis estimating interventional effects mapped to a target trial in R - Published in JOSS (2025)
https://github.com/amazon-science/causal-validation
Validate your causal models!
https://github.com/cyberagentailab/python-dte-adjustment
dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.
approaching-an-unknown-communication-system
Code/supplement for the paper "Approaching an unknown communication system by latent space exploration and causal inference"
matchtime
An R-Package to perform Time-Dependent Matching for Observational Data in Discrete and Continuous Time
simDAG
An R-Package to Simulate Simple and Complex (longitudinal) Data from a DAG and Associated Node Information
DPI
🛸 The Directed Prediction Index (DPI): Quantifying Relative Endogeneity of Outcome Versus Predictor Variables.
learning-representations-causal-inference
Code supplement for "Neuroevolutionary representations for learning heterogeneous treatment effects"
rath
Next generation of automated data exploratory analysis and visualization platform.
r-pipeline-development-workshop
Workshop on pipeline development and model deployment onto Kubernetes via Docker using R.
contsurvplot
An R-Package to Visualize the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome
bicausality
A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E).
Causality-in-Trustworthy-Machine-Learning
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
agrum
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).
rjaf
rjaf: Regularized Joint Assignment Forest with Treatment Arm Clustering - Published in JOSS (2025)
ml-treatment-effects
This is a repository of the master thesis on Casual Machine Learning for Heterogeneous Treatment Effects: An Empirical Application on Optimal Treatment Assignment.
https://github.com/1587causalai/causal-sklearn
Scikit-learn Compatible Causal Machine Learning Library - Based on CausalEngine™