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

Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)

Proceedings of AISTATS 2024

HyperITE

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

Exploitation of material consolidation trade-offs in multi-tier complex supply networks

Pytorch implementation of "Multi-view Integration Learning for Irregularly-sampled Clinical Time Series" (Under review, JBHI)

Decision curve analysis evaluates a predictor for an event as a probability threshold is varied, typically by showing a graphical plot of net benefit against threshold probability. By convention, t…

Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.

PhysioNet 2012 Challenge Time-series preprocessing pipeline

An index of algorithms for learning causality with data

A data index for learning causality.

VIP cheatsheets for Stanford's CS 229 Machine Learning

Code accompanying the paper "Empirical analysis of model selection for heterogeneous causal effect estimation"

Calculation of calibration slope, calibration intercept, and calibration in the large for binary prediction models in Python.

A playbook for systematically maximizing the performance of deep learning models.

Uplift modeling and causal inference with machine learning algorithms

Cyrillic-oriented MNIST. A dataset of Latin and Cyrillic letter images for text recognition.

Official code implementation for "Personalized Federated Learning using Hypernetworks" [ICML 2021]

machine learning and deep learning tutorials, articles and other resources

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 graphic…

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…

Investigating sensitivity of CATE estimators to the choice of hyperparameters.

A resource list for causality in statistics, data science and physics

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

Package for working with hypernetworks in PyTorch.