vsyrgkanis

optimistic_GAN_training

plugin_regularized_estimation

Code associated with paper: Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models, Chernozhukov, Nekipelov, Semenova, Syrgkanis, 2018

adversarial_gmm

Prototype code for paper: Adversarial Generalized Method of Moments, Greg Lewis and Vasilis Syrgkanis

asymmetric_common_value_auctions

Code accompanying paper "Information Asymmetries in Common Value Auctions with Discrete Signals"

omvb

Omitted variable bias in machine learned causal models

orthogonal_learning

Experiments for the paper: Orthogonal Statistical Learning

adversarial_reisz

Adversarial estimation of Reisz representers

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.

information_robust_econometrics_auctions

policy_learning_continuous_actions

Code accompanying paper on "Semi-Parametric Effecient Policy Learning with Continuous Actions", NeurIPS 2019

kdd2021-tutorial

EconML/CausalML KDD 2021 Tutorial

course-template

Fork this template to set up a new class landing page and build a syllabus for your class.

dml_sensitivity_python

Python code for Omitted Variable Bias in Causal Machine Learning

epigen

EpiGEN: an epistasis simulation pipeline

just-the-class

A modern, highly customizable, responsive Jekyll template for course websites.

mhcflurry

Peptide-MHC I binding affinity prediction

responsible-ai-widgets

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

shap

A game theoretic approach to explain the output of any machine learning model.