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ALP 301: Data-Driven Impact

This is a team-based course where students will work on a project to improve a product using data and experimentation. We will cover key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students will have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Topics include designing research and experiments, data analysis, experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research. The course involves three weekly meetings: two lectures and one lab. Lectures will focus on research methods and will provide examples of research outputs for students to discuss and evaluate. Labs will comprise technical training in data analysis and structured team meetings. Students will work in cross-functional teams of 5-6 with milest more »
This is a team-based course where students will work on a project to improve a product using data and experimentation. We will cover key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students will have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Topics include designing research and experiments, data analysis, experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research. The course involves three weekly meetings: two lectures and one lab. Lectures will focus on research methods and will provide examples of research outputs for students to discuss and evaluate. Labs will comprise technical training in data analysis and structured team meetings. Students will work in cross-functional teams of 5-6 with milestones throughout the quarter. The final deliverable will be a presentation that highlights the team's work and delivers actionable recommendations that draw from the team's research. The class will include a mix of students with different backgrounds and skills. Each team will have at least one member with significant experience with data analysis. This course is part of the GSB's new Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will: Learn research-validated foundations, tools, and practices, Apply these tools and learnings to a real project for an external organization, Create value for the organization by providing insights and deliverables, Be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to: Gain practical industry experience and exposure to the organization, its industry, and the space in which it operates, Build relationships in the organization and industry, and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages.
Terms: Spr | Units: 4
Instructors: Athey, S. (PI) ; Keleher, N. (SI)

CME 400: Ph.D. Research

Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

ECON 139D: Directed Reading

May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Akbarpour, M. (PI) ; Alsan, M. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Arora, A. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Baron, D. (PI) ; Bekaert, G. (PI) ; Bernheim, B. (PI) ; Bettinger, E. (PI) ; Bhattacharya, J. (PI) ; Bloom, N. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Brest, P. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chan, D. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chen, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; David, P. (PI) ; Diamond, R. (PI) ; Donohue, J. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fearon, J. (PI) ; Fitzpatrick, M. (PI) ; Foster, G. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Goda, G. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haak, D. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hamilton, J. (PI) ; Hammond, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hickman, B. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Horvath, M. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jarosch, G. (PI) ; Jones, C. (PI) ; Jost, J. (PI) ; Judd, K. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klausner, M. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Koudijs, P. (PI) ; Krueger, A. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Levin, J. (PI) ; Lynham, J. (PI) ; MaCurdy, T. (PI) ; Malmendier, U. (PI) ; McClellan, M. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Naylor, R. (PI) ; Nechyba, T. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Persson, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rossi-Hansberg, E. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Royalty, A. (PI) ; Rozelle, S. (PI) ; Sargent, T. (PI) ; Schaffner, J. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Sharpe, W. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Sorkin, I. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Topper, M. (PI) ; Vytlacil, E. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Williams, H. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI)

ECON 199D: Honors Thesis Research

In-depth study of an appropriate question and completion of a thesis of very high quality. Normally written under the direction of a member of the Department of Economics (or some closely related department). See description of honors program. Register for at least 1 unit for at least one quarter after your honors application is approved. Winter registration for one unit under the supervision of the Director of the Honors Program is mandatory for all honors students.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Alsan, M. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Arora, A. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Bekaert, G. (PI) ; Benkard, L. (PI) ; Bernheim, B. (PI) ; Bettinger, E. (PI) ; Bhattacharya, J. (PI) ; Bloom, N. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; David, P. (PI) ; Diamond, R. (PI) ; Donohue, J. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fearon, J. (PI) ; Fitzpatrick, M. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Goda, G. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hammond, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jarosch, G. (PI) ; Jones, C. (PI) ; Judd, K. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Koudijs, P. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Levin, J. (PI) ; Loeb, S. (PI) ; MaCurdy, T. (PI) ; McClellan, M. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Naylor, R. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Persson, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Rozelle, S. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Sorkin, I. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Williams, H. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI) ; Yurukoglu, A. (PI)

ECON 239D: Directed Reading

May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Akbarpour, M. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Arora, A. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Baron, D. (PI) ; Bekaert, G. (PI) ; Bernheim, B. (PI) ; Bhattacharya, J. (PI) ; Bloom, N. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; David, P. (PI) ; Diamond, R. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fitzpatrick, M. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Goda, G. (PI) ; Gould, A. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haak, D. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hammond, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hickman, B. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Horvath, M. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jarosch, G. (PI) ; Jones, C. (PI) ; Jost, J. (PI) ; Judd, K. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Koudijs, P. (PI) ; Kreps, D. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Levin, J. (PI) ; MaCurdy, T. (PI) ; Malmendier, U. (PI) ; McClellan, M. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Naylor, R. (PI) ; Nechyba, T. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Perez-Gonzalez, F. (PI) ; Persson, P. (PI) ; Pfleiderer, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rossi-Hansberg, E. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Royalty, A. (PI) ; Rozelle, S. (PI) ; Sargent, T. (PI) ; Schaffner, J. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Sharpe, W. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Somaini, P. (PI) ; Sorkin, I. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Topper, M. (PI) ; Vytlacil, E. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Williams, H. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI)

ECON 293: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consist primarily of empirical applications of the methods. Prerequisites: graduate level coursework in at least one of statistics, econometrics, or machine learning. Students without prior exposure to causal inference will need to do additional background reading in the early weeks of the course.
Terms: Spr | Units: 3
Instructors: Athey, S. (PI) ; Wager, S. (SI)

ECON 299: Practical Training

Students obtain employment in a relevant research or industrial activity to enhance their professional experience consistent with their degree programs. At the start of the quarter, students must submit a one page statement showing the relevance of the employment to the degree program along with an offer letter. At the end of the quarter, a three page final report must be supplied documenting work done and relevance to degree program. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Arora, A. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Baron, D. (PI) ; Bekaert, G. (PI) ; Bernheim, B. (PI) ; Bettinger, E. (PI) ; Bhattacharya, J. (PI) ; Bloom, N. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; David, P. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fitzpatrick, M. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Gould, A. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haak, D. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hamilton, J. (PI) ; Hammond, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hickman, B. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Horvath, M. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jarosch, G. (PI) ; Jones, C. (PI) ; Jost, J. (PI) ; Judd, K. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Krueger, A. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Levin, J. (PI) ; MaCurdy, T. (PI) ; Malmendier, U. (PI) ; McClellan, M. (PI) ; McKeon, S. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Naylor, R. (PI) ; Nechyba, T. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Persson, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rossi-Hansberg, E. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Royalty, A. (PI) ; Rozelle, S. (PI) ; Sargent, T. (PI) ; Schaffner, J. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Sharpe, W. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Sorkin, I. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Topper, M. (PI) ; Vytlacil, E. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Williams, H. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI)