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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.

machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

meps

This repository provides example code for loading and analyzing data from AHRQ's Medical Expenditure Panel Survey (MEPS). More information about the survey and access to public use data files is available on our website

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

sql-server-samples

Official Microsoft GitHub Repository containing code samples for SQL Server

template_ha

usbroadbandusagepercentages

We are publishing this dataset we developed as part of our efforts with Microsoft’s Airband Initiative to help close the rural broadband gap.

vega-datasets

Common repository for example datasets used by Vega-related projects