Starred repositories
Uplift modeling and causal inference with machine learning algorithms
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Official repository of "SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory"
Data and Program files for Causal Inference: The Mixtape
KDD 2024 2nd Workshop on Causal Inference and Machine Learning in Practice
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well a…
Template for data science competitions. Includes makefiles and Python scripts for feature engineering, cross validation, ensemble, etc.
Matched Markets is a Python library for design and analysis of Geo experiments using Matched Markets and Time Based Regression.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"
📚 Community guides for open source creators
A curated list of causal inference libraries, resources, and applications.
Salesforce open-source LLMs with 8k sequence length.
KDD 2023 Workshop - Causal Inference and Machine Learning in Practice: Use cases for Product, Brand, Policy and Beyond
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry thr…
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
Generate Tweet texts using OpenAI's GPT-3 based Davinci model
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
A Python package for causal inference in quasi-experimental settings
Replication files for Chernozhukov, Newey, Quintas-Martínez and Syrgkanis (2021) "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests"
Confidence intervals for scikit-learn forest algorithms
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.