stoddardg's Stars
awesomedata/awesome-public-datasets
A topic-centric list of HQ open datasets.
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Unstructured-IO/unstructured
Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
firmai/industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
online-ml/river
🌊 Online machine learning in Python
py-why/EconML
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 goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
TDAmeritrade/stumpy
STUMPY is a powerful and scalable Python library for modern time series analysis
matheusfacure/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
SeldonIO/alibi
Algorithms for explaining machine learning models
csinva/imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
manujosephv/pytorch_tabular
A standard framework for modelling Deep Learning Models for tabular data
koaning/scikit-lego
Extra blocks for scikit-learn pipelines.
davified/clean-code-ml
:bathtub: Clean Code concepts adapted for machine learning and data science. Now a free video series 😎 https://bit.ly/2yGDyqT
facebookresearch/balance
The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
alegonz/baikal
A graph-based functional API for building complex scikit-learn pipelines.
xLaszlo/datascience-fails
Collection of articles listing reasons why data science projects fail.
shobrook/sequitur
Library of autoencoders for sequential data
freelawproject/juriscraper
An API to scrape American court websites for metadata.
Quantco/glum
High performance Python GLMs with all the features!
JakeColtman/bartpy
Bayesian Additive Regression Trees For Python
koaning/memo
Decorators that logs stats.
AlaaLab/conformal-metalearners
[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"
msalganik/cos597E-soc555_f2020
Syllabus for COS 597E/SOC 555 Limits to Prediction, Fall 2020, Princeton University
khwilson/SentencingCommissionDatasets
Convert US Sentencing Commission Files to CSVs
MichaelLLi/evalITR
R Package for Evaluating Individualized Treatment Rules
tonyduan/hte-prediction-rcts
Predicting treatment effects from RCTs (Circulation: CQO 2019).
som-shahlab/ITE-model-selection
Yu-Group/stadisc
Code and notebooks to reproduce results in the staDISC paper.