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Labor Science in Healthcare and Education Research Symposium

Opportunity Lab Conference   |  past event  |  Oct 09 2020

Artificial intelligence, machine learning, and new data systems are continuing to revolutionize nearly every sector of the economy. The use of new algorithms and access to large granular datasets have led to new exciting research across different economic applications.

With generous support from Schmidt Futures, the Opportunity Lab’s Labor Science Initiative is coordinating a new community of scholars utilizing these tools to pose new questions about labor markets and the public sector. On October 9th, O-Lab assembled leading researchers in healthcare and education policy to present work that exemplifies the best of this initiative. The workshop offered a chance for faculty, students, and other colleagues to share insights on their research and to find commonalities in the work done across these two applications.


AM Session: Healthcare  (Moderator: Jon Kolstad)

10:00 – 10:35

Ziad Obermeyer (UC Berkeley)

Computational Medicine

10:40 – 11:15

Petra Persson (Stanford)

Family Spillover Effects of Misdiagnosis

11:20 – 11:55

Ben Handel (UC Berkeley)

The Social Determinants of Choice Quality: Evidence from Health Insurance in the Netherlands

PM Session: Education (Moderator: Jesse Rothstein)

12:00 – 12:35

Seth Zimmerman (Yale)

The Distribution of and Returns to Social Success at Elite Universities

12:40 – 1:15

Claudia Allende (University of Chicago)

Identifying the Equilibrium Effects of Informed

 School Choice

1:20 – 1:55

Chris Walters (UC Berkeley)

Using Centralized Assignment Data to Estimate School Value-added

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Video: Computational Medicine - Ziad Obermeyer | O-Lab Labor Science Symposium

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