Advanced Techniques in Applied Economics — Spring 2026

This course studies modern structured statistical methods for applied economics, with a focus on graphical models, causal discovery, latent-variable models, interference, and positive dependence.

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Announcements

• Apr 12: Please send to me your chosen topics for the final projects ASAP this week.
• Mar 21: The last two lectures are scheduled on May 4, 2:30-6:30pm.
• Mar 20: Slides for Lectures 1 and 2 are available.
• Mar 20: Suggested project topics are released.

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Instructor

Piotr Zwiernik
Email: piotr.zwiernik@upf.edu
Office hours: 20.202 by appointment

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Suggested reading

This is not a causal inference course but this area of data analysis relies heavily on similar ideas. I will try to make my lecture as self-contained as possible but some material that may be useful:

• R. Alexander, Telling stories with data.
• J. Peters, D. Janzing, & B. Schölkopf. Elements of causal inference: foundations and learning algorithms.
• V. Chernozhukov, C. Hansen, N. Kallus, M. Spindler, V. Syrgkanis, Applied Causal Inference Powered by ML and AI.
• S. Højsgaard, D. Edwards, S. Lauritzen. Graphical Models with R.
• The lecture slides and selected papers mentioned during the course

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Lectures and timeline (tentative)

Lecture	Topic	Slides
1	Conditional independence as structure	Lecture1
2	Gaussian and non-paranormal graphical models	Lecture2
3	DAGs, Markov equivalence, and interventions	Lecture3
4	Causal discovery, linear structural equation models, and non-Gaussian identification	Lecture4
5	Latent variable models — from trees to neural nets	Lecture5
6	Unobserved confounding and adjustments	Lecture6
7	Positive dependence and total positivity	Lecture7
8	Network interference and spillovers	Lecture8
9-10	Presentations

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Projects

See the proposed project topics.

How to give short technical talks?