Synthetic Control Methods

Princeton University is actively monitoring the situation around coronavirus (Covid-19) and the evolving guidance from government and health authorities. Any updates to this event, including rescheduling to a later date, will be posted to this website. For the latest University guidance for University members and visitors is available on the University’s Emergency Management website. 

Due to ongoing concerns and public safety and health restrictions associated with COVID-19, this workshop has been postponed.  Additional details will be provided at a later date.

DataX Logo with lines converging at an angle to a point and the words Synthetic Control Methods below it

Synthetic controls are widely applied to estimate the effects of policy interventions and other treatments of interests. The DataX Workshop on synthetic control methods seeks to provide an introduction to synthetic control methods for non-experts as well as an opportunity for researchers working on synthetic control methods to communicate new results, reach audiences outside their primary disciplinary fields, and seek potential collaborations. The two-day workshop will kick off with a 3-hour tutorial on synthetic control methods during the afternoon of Friday, September 11. Saturday, September 12 will be devoted to presentations and discussion of recent research contributions.
 

 

Workshop Organizers

  • Alberto Abadie

    • Alberto Abadie
      • Massachusetts Institute of Technology

  • Matias Cattaneo

    • Matias Cattaneo
      • Princeton University

Sponsors

We gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation and our Princeton University partners:

CSMLDataX Logo - A series of lines converging at a 45 degree angle to a point with DataX in the foreground

 

Upcoming Workshops

April 9-10, 2021 | Social Biases in Machine Learning and in Human Nature: What Social Scientists and Computer Scientists Can Learn From Each Other

This 2-day virtual workshop explores social biases in machine learning and in human nature; what social scientists and computer scientists can learn from each other. We bring cutting-edge, innovative sociology, social psychology, cognitive science, and computer science perspectives on the interplay between stereotyping and human and artificial intelligence.

Registration is free.   Registration form and workshop agenda are available on the workshop webpage:  https://biasinmachinehuman.squarespace.com/.  

July 13-14 | Accelerating Molecular Simulations with Machine Learning

Presented by Roberto Car, Chemistry and  Weinan E, Mathematics

Date TBD | Old and New Open Questions in Optimization

Presented by Nicholas Boumal, Electrical Engineering

Date TBD | Synthetic Control Methods

Presented by Matias Cattaneo, Operations Research and Financial Engineering
Event Page
Event Registration

Date TBD | Tutorial Workshop on Machine Learning for Experimental Science

Presented by Michael Churchill, Hantao Ji, William Tang
Princeton Plasma Physics Laboratory

Date TBD | Securing Machine Learning: Where Next?

Presented by Prateek Mittal, Electrical Engineering