The course is taught in English, so switch to english version please.
Poslední úprava: Schnellerová Dagmar, Ing. (01.12.2025)
PLEASE NOTE, this course is only starting during the second week of the semester.
This course introduces modern empirical methods in economics as applied to the technology sector, business, and policy. It focuses on modules covering experimentation, causal inference, demand estimation, and the intersection of economics with ML/AI. The course introduces key methodologies and demonstrates their applications through academic studies and case studies from business and policy.
The landscape of the economics profession has significantly changed with the rising importance of the technology sector. Economists have taken on an increasingly important role within this field. This course aims to help students develop the foundational skills that modern economists need and to introduce them to real-world problems that economists in technology face.
The course is taught by Bruno Baranek, who completed his PhD at Princeton University. He later worked at Amazon as a Senior Economist and Manager, where he developed technology tools to support Amazon’s retail business.
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
Literatura - angličtina
The course will use a combination of academic papers, articles about technology and papers summarizing methodological advances. See syllabus for exact papers. Following textbooks are recommended as resources for econometrics:
Chicago. Angrist, J. D., and Jorn-Steffen Pischke. 2008. Mostly Harmless Econometrics.
Causal Inference: The Mixtape. Scott Cunningham. Copyright Date: 2021
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
Metody výuky - angličtina
There will be one lecture each week. The course will require significant preparation for each session, including reading and summarizing one paper. These readings will then be discussed during the lecture.
Lectures will include discussions of empirical methodology, applications in economics, applications in the technology sector, as well as real-world problems faced by technology companies. The motivation behind this setup is to help students build an effective skill set transferable to solving real-world problems. To the best of my knowledge, combining theoretical knowledge of methodologies with detailed discussions and readings of their applications is the most effective way to achieve this in a classroom environment.
The discussions themselves are meant to develop these skills, not to test students. Please don’t hesitate to participate in discussions even if (or maybe better especially if)you’re not fully confident about your opinions on the readings.
Questions and comments are highly encouraged in class. You can also reach out outside of class by emailing me at technology.economics.ies@gmail.com. I am available for consultations. If you’d like to schedule one, please send me an email.
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
Požadavky ke zkoušce - angličtina
Components of final grade
Your final grade will consist of four parts with the following weights:
Weekly reading of a paper each with a written referee report:: 40%
Short presentation of an interesting technology application: 10%
Written final exam: 25%
Project consisting of either literature review or empirical work 25%
There might also be bonus points for active class participation.
Grades ECTS
91-100 A
81-90 B
71-80 C
61-70 D
51-60 E
0-50 F
Referee Report
Each week there will be an assigned reading of an economics paper. These papers will be collectively discussed in the lecture. Prior to the lecture students are required to send a short referee report on that topic to the lecturer. The referee report should be rather short (approximately one page) and summarize the content, strengths, and weaknesses of the paper. These can be done in groups of up the 3 people.
Presentation
Students will prepare a short presentation summarizing an interesting technology topic. For example, how exactly do the Google advertisement auctions work? How does approximately Netflix’s personalization work? The exact topic is fully up to the students. Teams of up to three people are admissible.
Final paper
Students will prepare a paper (5-10 pages) within their chosen topic. The areas can be either a conducted empirical study. This can be done using ready to use data or can be a replication of an existing study. The second option is to conduct a literature review on a given topic. Areas of study are up to students as long as they somewhat tie to the content of the course (empirical methods and technology application). Teams up to three are admissible.
Final test
There will be a final test covering the content of the course.
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
Sylabus - angličtina
Note, this course is only starting during the second week of the semester.
Module 1: A/B Testing (Randomized Experiments)
Readings:
Gallo, A. (2017). A refresher on A/B testing.Harvard Business Review, June 28, 2017hbr.org.
Athey and Imbens (2016): The Econometrics of Randomized Experiments∗
Applications:
Bloom, N. et al. (2015). Does working from home work? Evidence from a Chinese experiment.Quarterly Journal of Economics, 130(1), 165–218ideas.repec.org.
Blake, T., Nosko, C., & Tadelis, S. (2015). Consumer heterogeneity and paid search effectiveness: A large-scale field experiment.Econometrica, 83(1), 155–174nber.org.
Case Studies:
Zalando Engineering. (2019). Experimentation Platform at Zalando (Part 1: Evolution)engineering.zalando.com.
Scott Cunnigham: Causal Inference The mixtape. Online textbook
Angrist, J., & Pischke, J. (2010). The credibility revolution in empirical economics. Journal of Economic Perspectives, 24(2), 3–30aeaweb.org. Collision (2022) Methods to estimate causal effects
Athey, S., & Imbens, G. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3–32aeaweb.org.
Lee, D., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281–355aeaweb.org.
Applications:
Card, D., & Krueger, A. B. (1994). Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania. American Economic Review, 84(4), 772–793nber.org.
Luca, M. (2016). Reviews, reputation, and revenue: The case of Yelp.com. Harvard Business School Working Paper 12-016 (rev. 2016)hbs.edu.
Barron, K., Kung, E., & Proserpio, D. (2020). The effect of home-sharing on house prices and rents: Evidence from Airbnb. SSRN Working Paper (NBER w30068)papers.ssrn.com.
Case Studies:
Spotify Research. (2023). Encouragement designs and instrumental variables for A/B testing engineering.atspotify.com.
Uber Engineering. (2019). Using causal inference to improve the Uber user experienceuber.com.
Module 3: Demand Estimation
Readings:
Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63(4), 841–890its.caltech.edu. (Structural model of demand and supply for U.S. cars, foundational random-utility model paper.)
Berry and Haile: Foundations of demand estimation
Applications:
Gentzkow, M. (2007). Valuing new goods in a model with complementarity: Online newspapers. American Economic Review, 97(3), 713–744aeaweb.org. (Demand estimation combining print and online newspaper competition.)
Hausman, J., Leonard, G., & Zona, J. (1994). Competitive analysis with differentiated products (beer).Annales d’Économie et de Statistique, No. 34, 143–157econpapers.repec.org.
Cohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of Uber. NBER Working Paper No. 22627nber.org.
Case Studies:
Airbnb Engineering. (2018). Learning market dynamics for optimal pricingmedium.com.
Module 4: Topics in Economics & AI/ML
Readings:
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. JEP 31(2): 87–106aeaweb.org.
Agrawal, A., Gans, J., & Goldfarb, A. (2019). The economics of artificial intelligence: An agenda. University of Chicago Press (NBER volume).
MIT News. (2024, Dec 6). What do we know about the economics of AI? MIT Newsnews.mit.edu.
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
Vstupní požadavky - angličtina
There are no explicit prerequisites for graduate or advanced undergraduate students to enroll in the class. However, a foundational knowledge of statistics and basic econometrics will be assumed—specifically, understanding how to move from an equation to an estimation procedure. Students without this background are welcome to enroll but should expect to invest additional time to catch up on these materials.
Poslední úprava: Baránek Bruno, M.A., M.Sc., Ph.D. (16.01.2026)
