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.

Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

A General Causal Inference Framework by Encoding Generative Modeling

OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.

Machine learning based causal inference/uplift in Python

My 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

BITES: Balanced Individual Treatment Effect for Survival data

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

Implementation of neural network algorithm for estimation of heterogeneous treatment effects and propensity scores described in Farrell, Liang, and Misra (2021)

[ICLR24] A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error

clairvoyance2: a Unified Toolkit for Medical Time Series