dmachlanski

pykan

Kolmogorov Arnold Networks

causalnex

A Python library that helps data scientists to infer causation rather than observing correlation.

causallib

A Python package for modular causal inference analysis and model evaluations

causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

doubleml-for-py

DoubleML - Double Machine Learning in Python

tabular-benchmark

mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.

causica

cfrnet

Counterfactual Regression

dragonnet

project-azua

Data Efficient Decision Making

causaltune

AutoML for causal inference.

ENCO

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

benchpress

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

cause2e

The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).

SITE

DADA

code for "DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification"

RVAE_MixedTypes

Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)

npci

Non-parametrics for Causal Inference

Meta_learner-for-Causal-ML

This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.

counterfactual-cv

(ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’

skgrf

scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library

caussim

Simulations for predictive model selection in causal inference

EconML-with-R

How to use EconML within R

AutoCD

Towards Automated Causal Discovery

dlts_paper_code

Repository for code in paper Deep Learning in Target Space

CATESelection

Sklearn-style implementations of model selection criteria for CATE estimation

CEILS

Counterfactual Explanations as Interventions in Latent Space (CEILS) is a methodology to generate counterfactual explanations capturing by design the underlying causal relations from the data, and at the same time to provide feasible recommendations to reach the proposed profile.

qprobing

The qprobing package provides functionality for evaluating the effectiveness of quantitative probing as a method for validating causal models. Developed by Daniel Grünbaum (@dg46).

OCT

Out-of-sample Causal Tuning