Teaching the Frontier
Guest lectures in my courses, taught by PhD students
Each term I set aside a few guest-lecture and tutorial slots in my undergraduate and MA courses for topics outside my own expertise. PhD students, from Economics or any other department, apply to teach one. You get classroom experience; my students get a specialist on a topic I would otherwise cover less well.
Several of my former teaching assistants have used a guest lecture in my courses for their teaching portfolios and job applications. This page makes that a standing offer.
A session is one 60–80 minute lecture or a tutorial tied to a core module of the course. When there is room, I open the session to other undergraduates as well.
What I offer
- Teaching experience with an undergraduate or MA class, on a topic you know well.
- A written teaching evaluation. I attend your session and send you written feedback afterwards.
- A reference. Having seen you teach, I can write a letter for teaching-focused positions.
- A low-stakes setting to try a format or materials and collect student feedback.
Open topics
Sessions build on the existing syllabus, so yours should connect to what students already know. If you work on something related that is not listed, propose it.
ECO1465 · Applied Causal Machine Learning (MA/PhD)
How large language models work
- Transformers, attention, and embeddings, explained for economists
- LLMs as a research tool: text as data, classification, structured extraction from documents
- Retrieval-augmented generation and vector databases for literature and policy work
- What LLMs cannot do: hallucination, evaluation, and where causal reasoning still needs a human
ECO1465 · Applied Causal Machine Learning (MA/PhD)
Advances in causal machine learning
- Double/debiased machine learning beyond the course introduction
- Heterogeneous treatment effects with causal forests in practice
- Synthetic controls and modern difference-in-differences
ECO204 · Microeconomic Theory and Applications
Behavioural economics
- Where the standard model breaks: reference points, loss aversion, prospect theory
- Nudges and choice architecture, with policy examples
- Present bias and self-control, connected to intertemporal choice in the course
- What lab and field experiments can and cannot tell us about behaviour
ECO204 · Microeconomic Theory and Applications
Market design and matching
- Auctions, from theory to spectrum sales and ad markets
- Matching markets: school choice and kidney exchange
- How design fixes markets that prices alone cannot
ECO225 · Data Tools for Economists
Data visualization with Tableau
- Building an interactive dashboard from a real economic dataset
- When Tableau beats a static Python chart, and when it does not
- Design choices that make a visualization honest and readable
- Publishing a dashboard as part of a research portfolio
ECO225 · Data Tools for Economists
Geospatial and network data
- Mapping economic data with GIS tools beyond the course's mapping module
- Network analysis: trade, citations, social graphs
- Satellite and remote-sensing data as an economic source
Topics rotate by term. Availability depends on the course calendar and on how a proposed session fits the students' current level.
How it works
- Check the open topics above, which I update each term.
- Apply. The form asks for the topic, a paragraph on your approach, and how it connects to the course.
- Plan. If selected, we meet once to match the session to the students' level and settle materials and timing.
- Teach. You give the lecture or tutorial. I attend.
- Feedback. You receive a written evaluation, and I can act as a reference afterwards.
Who can apply
PhD students in Economics or any other department whose work relates to an open topic. No prior lecturing experience is needed. Advanced MA students may be considered for tutorial slots.
Apply
Application form. I reply within the term. Questions to nazanin.khazra [at] utoronto.ca.