causal-models
There are 100 repositories under causal-models topic.
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.
mckinsey/causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
FenTechSolutions/CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
BiomedSciAI/causallib
A Python package for modular causal inference analysis and model evaluations
jvpoulos/causal-ml
Must-read papers and resources related to causal inference and machine (deep) learning
erdogant/bnlearn
Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
DataCanvasIO/YLearn
YLearn, a pun of "learn why", is a python package for causal inference
cdt15/lingam
Python package for causal discovery based on LiNGAM.
msuzen/looper
A resource list for causality in statistics, data science and physics
TimeLovercc/Awesome-Graph-Causal-Learning
A list of Graph Causal Learning materials.
declare-lab/RECCON
This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.
OscarEngelbrektson/SyntheticControlMethods
A Python package for causal inference using Synthetic Controls
uhlerlab/causaldag
Python package for the creation, manipulation, and learning of Causal DAGs
JackHCC/Awesome-Uplift-Model
🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】
maxwshen/iap-cidl
Causal Inference & Deep Learning, MIT IAP 2018
qitianwu/GraphOOD-EERM
The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
changliu00/causal-semantic-generative-model
Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)
rsyi/pylift
Uplift modeling and evaluation library. Actively maintained pypi version.
mikenguyen13/data_analysis
Streamline a data analysis process
felixleopoldo/benchpress
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
MLResearchAtOSRAM/cause2e
The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).
saezlab/CARNIVAL
CAusal Reasoning for Network Identification with integer VALue programming in R
leizhang-geo/ST-CausalConvNet
A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.
diviyank/SAM
Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
zlxy9892/ST-CausalConvNet
A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.
StatisticalRethinkingJulia/StructuralCausalModels.jl
Initial look at directed acyclic graph (DAG) based causal models in regression.
neildhir/DCBO
Dynamic causal Bayesian optimisation
paul-krug/pytorch-tcn
(Realtime) Temporal Convolutions in PyTorch
santikka/causaleffect
causaleffect: R package for identifying causal effects.
trislett/TFCE_mediation
Fast regression and mediation analysis of vertex or voxel MRI data with TFCE
VE-FORBRYDERNE/mtj-softtuner
Create soft prompts for fairseq 13B dense, GPT-J-6B and GPT-Neo-2.7B for free in a Google Colab TPU instance
agrumery/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).
LeihuaYe/Causal-Inference-Using-Quasi-Experimental-Methods
Causal Inference Using Quasi-Experimental Methods
tjohnson250/overview_causal_inference
A Brief Overview of Causal Inference (xaringan presentation)
krassowski/jupyterlab-dagitty
JupyterLab renderer of dagitty causal diagrams
uio-bmi/dagsim
A framework and specification language for simulating data based on graphical models