Presentations
Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability
October 2023, Yale (Cowles Foundation)
Slides
Economics and Machine Learning: What can they teach each other?
September 2023, Berlin
Slides
Recording
Employing the unemployed of Marienthal: Evaluation of a guaranteed job program
March 2023, IHS
Slides
Recording
The political economy of AI: Who controls the means of prediction?
February 2023, Oxford series “What economists really do”
Slides
Recording
April 2023, MIT SERC Symposium
Recording
Democratic control of the means of prediction
October 2022, Oxford Ethics in AI Colloquium
Slides
Recording
Adaptive maximization of social welfare in theory and practice
October 2022, Harvard and MIT
Slides
Discussion of:
Increasing the uptake of long-acting reversible contraceptives among adolescents and young women in Cameroon
July 2022, NBER Summer Institute
Slides
Adaptive maximization of social welfare
June 2022, ENSAI Artificial Intelligence and Economic Decision Making conference, Rennes
Slides
Recording at ENSAI 2022
Rationalizing Pre-Analysis Plans: Statistical Decisions Subject to Implementability
January 2022
Slides
Recording for BITSS 2022
Employing the unemployed of Marienthal:
Evaluation of a guaranteed job program
January 2022, Marie Jahoda memorial conference
Slides
How to run an adaptive field experiment
December 2021, Field Days conference
Slides
Machine learning for policy
November 2021, deputy ministers of Pakistan
Slides
Learning by matching
September 2021, EAAMO conference
Slides
Social foundations for statistics and machine learning
London, September 2021
Slides
Piloting new social safety nets: Evaluation of a job guarantee program and of a basic income program
Vienna, June 2021
Slides
Recording
Statistical decision theory cannot justify
randomization or pre-registration for experiments.
Sorbonne, Philosophy of Science Workshop, June 2021
Slides
Philosophical questions about machine learning theory:
Online Learning, Bandit Algorithms, and Reinforcement Learning
Harvard Philosophy of Causality Workshop, March 2021
Slides
Fairness, equality, and power in algorithmic decision making
Oxford Law School, March 2021
Slides
Recording for Informs 2021
Probably approximately correct learning and adversarial online learning
Alan Turing Institute, Economics and ML reading group, February 2021
Slides I
Slides II
The social impact of algorithmic decision making: Economic perspectives
Stanford, October 2020
Slides
Basic income and basic income experiments
July 2020
Slides
Recording
What do we want? And when do we want it?
Alternative objectives and their implications for experimental design.
May 2020
Slides
Recording of presentation in the Chamberlain online seminar
(With discussions by David McKenzie and Max Tabord-Meehan)
Adaptive combinatorial allocation: How to use limited resources while learning what works
May 2020
Slides
Fairness, equality, and power in algorithmic decision making
May 2020
Slides
No data in the void
Economics for Inclusive Prosperity Conference, Harvard Kennedy School, March 27, 2020
Slides
Adaptive treatment assignment in experiments for policy choice
Causal Machine Learning Workshop St. Gallen, January 20, 2020
Slides
Experiments designed to help the participants
IRC Jordan, July 10, 2019
Slides
Conference “Statistics in a social context”
Harvard, May 10-11, 2019
Conference announcement, schedule, and links to talks
Adaptive treatment assignment in experiments for policy choice (short version)
MIT, May 18, 2019
Slides
Statistics in a Social Context - Opening remarks
Harvard, May 10, 2019
Slides
Designing basic income experiments
Jain Family Institute, April 12, 2019
Slides
Machine learning, shrinkage estimation, and economic theory
Sciences Po, Friday, December 14, 2018
Slides
Which findings get published? Which findings should be published? (short version)
BITSS Annual Meeting, UC Berkeley, Monday, December 10, 2018
Slides
Video
Causality and randomization
Harvard Philosophy Causality Conference, Friday, November 2, 2018
Slides
Which findings get published? Which findings should be published?
Bocconi, Tuesday, September 4, 2018
Slides
Which findings should be published
Cologne, Thursday, August 30, 2018
Slides
How to use economic theory to improve estimators
Montreal, Wednesday, June 27, 2018
Slides
Optimal taxation and insurance using machine learning
Machine Learning in Economics and Econometrics conference, Munich, Tuesday, May 29, 2018
Slides
Habilitationsvortrag: Machine learning, shrinkage estimation, and economic theory
WU Wien, Friday, May 25, 2018
Slides
Estimating Risk
Gary Chamberlain Retirement Conference, Friday, May 4, 2018
Slides
“Approximate Cross-Validation,” and “Dynamic Experiments for Policy Choice”
Harvard, Monday, April 23, 2018
Slides
Choosing among regularized estimators in empirical economics
AEA Session: Machine Learning for Policy Research, Friday, January 5, 2018
Slides
Identification of and correction for publication bias
Sciences Po, Wednesday, December 13, 2017
Slides
Technischer Wandel und der Arbeitsmarkt
Hauptverband der österreichischen Sozialversicherungsträger, Friday, October 20, 2017
Slides
Empirical research on economic inequality: Normative considerations and empirical practice
IHS Wien, Monday, May 15, 2017
Slides
International corporate taxation: Problems and a reform proposal
Re:publika Berlin, Monday, May 8, 2017
Slides
Uniformity and the delta-method
Monday, October 27, 2014
Slides
Who wins, who loses? Tools for distributional policy evaluation
Friday, September 26, 2014
Slides
Video
Why experimenters should not randomize, and what they should do instead
Wednesday, February 27, 2013
Slides
Video
Partial identification, distributional preferences, and the welfare ranking of policies
Friday, April 27, 2012
Slides
Nonparametric inference on the number of equilibria
Thursday, May 5, 2011
Slides
Identification in a model of sorting with social externalities and the causes of urban segregation
Tuesday, March 15, 2011
Slides
Identification in Triangular Systems Using Control Functions
Wednesday, November 10, 2010
Slides