vsyrgkanis

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"

course-template

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

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.

epigen

EpiGEN: an epistasis simulation pipeline

information_robust_econometrics_auctions

just-the-class

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

kdd2021-tutorial

EconML/CausalML KDD 2021 Tutorial

optimistic_GAN_training

policy_learning_continuous_actions

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

vsyrgkanis/optimistic_GAN_training

vsyrgkanis/adversarial_gmm

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

vsyrgkanis/asymmetric_common_value_auctions

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

vsyrgkanis/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.

vsyrgkanis/information_robust_econometrics_auctions

vsyrgkanis/policy_learning_continuous_actions

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

vsyrgkanis/course-template

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

vsyrgkanis/epigen

EpiGEN: an epistasis simulation pipeline

vsyrgkanis/just-the-class

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

vsyrgkanis/kdd2021-tutorial

EconML/CausalML KDD 2021 Tutorial

vsyrgkanis/mhcflurry

Peptide-MHC I binding affinity prediction

vsyrgkanis/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.

vsyrgkanis/shap

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