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

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

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

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

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

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

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

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

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

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

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