Unfortunately many articles are behind paywall now, especially old Bloomberg ones.

Field Overview

• Breaking the Spell That Grips Economics Noah Smith - Bloomberg

• Economics Struggles to Cope With Reality Noah Smith - Bloomberg

• All of a Sudden, Economists Are Getting Real Jobs Noah Smith - Bloomberg

• Data Geeks Are Taking Over Economics Noah Smith - Bloomberg

• Theory Versus Data? You Shouldn't Have to Choose Noah Smith - Bloomberg

• Goodbye, Ivory Tower. Hello, Silicon Valley Candy Store Steve Lohr - NYT

• How Economics Went From Theory to Data Justin Fox - Bloomberg

• Quora Session with Susan Athey Susan Athey- Stanford GSB Prof.

• Economics Gets Real Noah Smith - Bloomberg

• Hunting for a Hot Job in High Tech? Try 'Digitization Economist Roberta Holland - Working Knowledge

• Uber’s secret weapon is its team of economists Alison Griswold - Quartz

• Why Uber Is an Economist’s Dream Stephen J. Dubner - Freakonomics

• Susan Athey Interview: Applying Machine Learning to the Economy Stanford GSB

• Sexy and Social Data Scientists Forbes Article

• Computer Science Is Coming for Economics Vishal Wilde

• Micro stars, macro effects Economist Article

• Economists are prone to fads, and the latest is machine learning Economist Article- 2012

  • A critical piece by Economist on the surge of ML in Econ. This was followed by counter argument by Noah Smith

• Are current trends in econ methodology just fads? Noah Smith

• Two Cousins Meet Avinash Tripathi

• Causal Inference and Machine Learning Guido Imbens

Articles by Practitioners

• Teconomics- Economists in Tech - Emily Glassberg Sands & Duncan Gilchrist

  • Machine Learning for Decision Making - Emily Glassberg Sands & Duncan Gilchrist

  • How to Use Machine Learning to Accelerate A/B Testing - Emily Glassberg Sands & Duncan Gilchrist

  • Machine Learning Meets Instrumental Variables - Emily Glassberg Sands & Duncan Gilchrist

• Stanford is Using Machine Learning on Satellite Images to Predict Poverty- Analytics Vidhya

• Economic Predictions with Big Data: The Illusion of Sparsity - NY Federal Reserve

• Refining the “science” of political science (MIT)- MIT PolSc

  • Political Methodology Lab- MIT PolSc

Journal of Economics Perspective Symposiums

• Recent Ideas in Econometrics (Spring 2017)

  • The State of Applied Econometrics: Causality and Policy Evaluation - Susan Athey & Guido W. Imbens

  • Machine Learning: An Applied Econometric Approach - Sendhil Mullainathan & Jann Spiess

  • The Use of Structural Models in Econometrics - Hamish Low & Costas Meghir

  • Twenty Years of Time Series Econometrics in Ten Pictures - James H. Stock & Mark W. Watson

  • Identification and Asymptotic Approximations: Three Examples of Progress in Econometric Theory - James L. Powell

• Big Data (Spring 2014)

  • Big Data: New Tricks for Econometrics - Hal Varian

  • High-Dimensional Methods and Inference on Structural and Treatment Effects - Alexandre Belloni, Victor Chernozhukov, Christian Hansen

  • Political Campaigns and Big Data - David W. Nickerson & Todd Rogers

  • Privacy and Data-Based Research - Ori Heffetz & Katrina Ligett

• Econometrics Tools (Fall 2011) - Various papers and authors

• Con out of Economics (Spring 2010)

  • Taking the Dogma out of Econometrics: Structural Modeling and Credible Inference - Nevo and Whinston

  • But Economics Is Not an Experimental Science - C. Sims

  • The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics - Angrist and Pischke

  • A Structural Perspective on the Experimentalist School - M.P Keane

• Beyond Big Data - Hal Varian

Susan Athey Papers- Prof. of Economics @ Stanford Graduate School of Business

• Economists (and Economics) in Tech Companies

• The Impact of Machine Learning on Economics

• Beyond prediction: Using big data for policy problems

• Machine Learning Methods for Causal Effects

Susan Athey Videos - Prof. of Economics @ Stanford Graduate School of Business

• How Big Data Is Changing Economies

• Machine Learning and Causal Inference for Policy Evaluation

  • Machine Learning and Causal Inference for Policy Evaluation

• Sackler Big Data Colloquium

• Artificial Intelligence: The Economic and Policy Implications-Keynote

Hal Varian Videos - Chief Economist @ Google

• Google and social science research

• Using Google Data for Short-term Economic Forecasting

• Economics & Technology: Careers Blending Two Fields

• Data On Purpose | Do Good Data: Casual Inference Meets Big Data

Sendhil Mullainathan Videos - Prof. of Economics @ Harvard University

• Machine Intelligence and Public Policy

• Machine Learning and Prediction in Economics and Finance

• Machine Learning: What's in it for Economics - Playlist Univ. of Chicago

• Machine Learning Meets Economics: Using Theory, Data, and Experiments to Design Markets

• Smarter Algorithms, Better Policy

Other Videos

• Why Economics Needs Data Mining Cosma Shalizi(CMU)

• Machinistas meet Randomistas: useful ML tools for Empirical Researchers Esther Duflo

• The Economics of Artificial Intelligence & Income Distribution Jeffrey Sachs

• Human Decisions and Machine Predictions Jon Kleinberg (Cornell)

• The Challenge of Big Data for the Social Sciences Kenneth Benoit, Kenneth Cukier

• Data Science from the Perspective of an Applied Economist Scott Nicholson

• From Economist to Data Scientist: How our discipline can participate in the growth of analytics Kenneth Sanford

Academics

• Matthew Harding : University of California - Irvine

  • Check the Deep Data Lab

• David Broockman : Stanford University

• Andrew B. Hall : Stanford University

• Ariel Procaccia : Carnegie Mellon University

• Dario Sansone : Phd Candidate Georgetown University

  • Dario has compiled an informative list on ML and Economics

• Soubhik Barari : Harvard University

Industry

• Companies like Airbnb, Microsoft and Amazon have huge teams which is filled with Economists

• Amazon Economist Jobs

  • Also check Economics @ Amazon

• Microsoft Research: Economics Group

Economists at various companies

• MIT has started a new Major for Undergraduates. The Program aims to impart students skills from Data Science and Economics

  • News Announcement
  • Economics 6-14: Course Overview
  • Course Content

• Machine Learning and Econometrics (Susan Athey, Guido Imbens) - Stanford University

  • There are videos and course material available
  • Course Material

• ECON 45: Using Big Data to Solve Economic and Social Problems - Raj Chetty @ Stanford University

  • Raj Chetty and team will use data from Facebook to study Inequality in America
  • Combining satellite imagery and machine learning to predict poverty

• Data Science for Politics - Stanford University

• Machine Learning and Data Science in Politics - In Song Kim @ MIT

• Machine Learning and Causal Inference - Susan Athey @ Stanford University

• Big Data - Daniel Bjorkegren @ Brown University

• Data Analysis for Social Scientists- Esther Duflo & Sara Fisher @ MITx

• R-Based High Performance Computing for Social Science- Soubhik Barari @ MIT

• Field Experiments - David Reiley @ UC Berkley

• Industrial Organization and Data Science - Justin Rao @ Microsoft

• Data Science for Game Theory and Pricing - Jacob @ Microsoft

• Designing the Digital Economy - Glen Weyl @ Yale

  • Digitization is transforming a variety of markets from personal transportation services to advertising. This course explores the economic tools (market design, price theory, causal inference, etc.) and technical tools from computer science (machine learning, the analysis of algorithms, user interface design, etc.) students need to contribute meaningfully to this transformation.

• Enviornmental Economics and Data Science - Grant McDermott @ University of Oregon

  • Lectures

• Data for Sustainable Development - Marshall Burke, Stefano Ermon, David Lobell @ Stanford University

  • The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter
  • Syllabus
  • Projects