Sitemap - 2024 - Scott's Mixtape Substack
S4E10: Ted Joyce, Health Economist, CUNY
Spring 2025 workshops plus camping and personal things
Closing my tabs: the Kerouac edition
How to tell if ChatGPT wrote the students essays
S4E9: Francine Blau, Gender and Labor Economics, Cornell University
Short gap versus Long differences in diff-in-diff event studies: part 2
Short gap versus Long differences in diff-in-diff event study: Part 1
A 700 page gorilla of a book has been sent Yale University Press
S4E8: Jann Spiess, Machine Learning and Causal Inference, Stanford
Closing my browser tabs out: Saturday edition
Back home and under the weather with t-6 days
Closing my Saturday browser tabs
Maybe Ken Chay is the Bridge between Rubin and Princeton, but Only Once He Gets to Berkeley
Implementing my diff-in-diff checklist for the book using county level crime data
S4E7: Elizabeth Cascio, Labor Economist, Dartmouth
Update on the Mixtape Revision
Closing out my open browser tabs
The Final Stretch: Another Mixtape Update
Rant in Defense of the Apple Vision Pro
Update on the Mixtape Second Edition Revisions
Diff-in-Diff Chapters Done, On To Synthetic Control
Can Generative AI Predict Elections? A Qualified Answer
Update on Revision of Mixtape: Countdown t-24
S4E6: Timothy Bartik, Labor Economics, Upjohn Institute
Update on Progress on the Mixtape Revisions
Tips and Thoughts about AI for Research and Teaching College Students
Exploring the World of Charles Manski with an AI Generated Podcast Doing a Deep Dive on His Vita
Advanced Transcript: Tim Bartik, Labor Economics, Upjohn Institute
Formalized Argument for Design Being Longer in QCD than RCT: Part 3
An Argument for the QCD (Quasi-Experimental Controlled Design): Part 2
Hidden Curriculum Workshop with Mark Anderson and Dan Rees
S4E5: Miikka Rokkanen, Consumer Behavior Analytics, Amazon
Diff-in-diff papers and their impact
Design Time is a Substitute for Controlled Randomization: Part 1
Causal Panel Workshop and ML and Causal Inference Workshop: a Public Service Announcement
Advanced Transcript: Miikka Rokkanen, Customer Behavior Analytics, Amazon
How I Asked ChatGPT to Help Me Become a Better Grader
My Annual Prediction Ritual of the Nobel Prize in Economics
S4E4: Maya Rossin-Slater, Health Economist, Stanford
Riding the moving sidewalk and the art of prompt coding with LLMs
Advanced Transcript: Interview with Maya Rossin-Slater of Stanford University
This and That Causal Inference
ElectionGPT Predicts Harris to get between 306 and 316 electoral college votes (September 25th)
S4E3: Mohammad Akbarpour, Microeconomic Theory, Stanford
Advanced Transcript: Interview with Mohammad Arkbarpour
Effect of AI on learning versus effect of AI on completed learning tasks
Strawberry Use Cases #1: Chain of Thought in Back-of-the-Envelope Math
Causal Inference Workshop and Causal AI
Does o1 engage in counterfeit reasoning and if so can we detect it?
S4E2: N. Greg Mankiw, Macroeconomics, Harvard
Next-Day Podcast Transcript: Interview with Dr. N. Greg Mankiw of Harvard University
Who Brings Potential Outcomes Notation Into Economics?
Children and Grandchildren of the Revolution: Part 2
Office Hours, Forward vs Reverse Causal Inference and Peter Hull's Design-Based Inference Workshop
Use Cases of ChatGPT-4o: Take me to the Iowa Writers Workshop
Update on ElectionGPT but first a story from ChatGPT
S4E1: Janet Currie, Health and Children, Princeton
Next-Day Podcast Transcript: Interview with Dr. Janet Currie of Princeton University
Narrative-Driven Predictions: Explaining Our ChatGPT-4o-mini Experiment in Election Forecasting
Pedro's DID Checklist: Step 4(i), more on selection
Children and Grandchildren of the Revolution: Part 1
Two Joke Tee Shirts and an AI Fueled Pet-Empathy Questionnaire
Does the PhD hurt mental health, how is it measured, is it selection? A little of all three
Orley Ashenfelter was the Father of the Revolution
Update to Podcast and Countdown to New Fall Mixtape Sessions Workshops
119 Days Until the Mixtape Revision is Due
Side Effects, Partial Defiance, Partial Monotonicity Violation
New Agenda: Educating 1 million people in causal inference
Leaving the Basque Country, Almost Home
Lesser-Known Biases in Standard OLS Specifications for Difference-in-Differences with Covariates
S3E26: Javier Gardeazabal, Political Economy and Econometrics, University of the Basque Country
It's that time of year again: Mixtape Sessions Fall 2024 Lineup!
S3E25: Avinash K. Dixit, Microeconomics, Princeton University
Perfect Doctor Violates Parallel Trends: Step 4(e) in Pedro's DiD Checklist
S3E24: David Autor, Labor Economist, MIT
Personal Reflections: day one in San Sebastián, the Basque Country
Goodbye Scotland. Hello San Sebastian
S3E23: Adriana Lleras-Muney, Labor Economist, UCLA
Pedro’s checklist 4c: Ashenfelter Dip, Parallel Trends but Non-zero Pre-trends
S3E22: Manisha Shah, Development Economist, UC Berkeley (episode 100!)
Selection on Y(0) and Parallel Trends: Anticipating Step (4a) in Pedro's Diff-in-Diff Checklist
Step (3) in Pedro’s diff-in-diff checklist: plotting the outcome
(Repeat): S2:E1 Interview with Jeff Wooldridge, Economist and Econometrician
Step (2) of Pedro's DiD checklist: documenting how many units are in each cohort
My Review of the Apple Vision Pro After One Month
S3E21: Ashesh Rambachan, Predictive Algorithms and Causal Inference, MIT
Pedro’s diff in diff checklist: step 1) plotting the rollout
Lalonde - 40 years later (Imbens and Xu's review): My First Impressions
S3E20: Henry Farber, Labor Economist, Princeton
Yummy and Inexpensive Food in Torino, Friends in Torino and my Mixtape Revision Summer Plans
Saturday links (Torino edition)
Leaving Madrid, Heading to Torino
CodeChella Madrid -- Initial Reflections
S3E19: Sarah Miller, Health Economist, Michigan
S3E18: E. Glen Weyl, Economist and Author, Microsoft
S3E17: Matthew Jackson, Economics of Networks, Stanford
Saturday morning cleaning out my browser by sharing links and closing tabs
Using ChatGPT-4 to analyze my class evaluations
CodeChella Madrid, My Summer Plans in Europe, and a Video Review of my new Apple Vision Pro
S3E16: Bruce Sacerdote, Labor Economist, Dartmouth
Saturday morning links: scanning articles on the future of AI and society
The Pinocchio Effect, Economies of Scale and Market Concentration in AI
S3E15: Peter Boettke, Austrian Economics, George Mason University
The 2018 Farm Bill Apparently Legalized THC
S3E14: Jesse Rothstein, Labor Economist, UC Berkeley
CAREER is a Transformer Model Used to Predict Occupational Outcomes
Introducing "Design-Based Regression Inference" Workshop Taught by Peter Hull
S3E13: Martin Gaynor, Health Economist, Carnegie Mellon/DOJ
ChatGPT Can Predict the Future When it Tells Stories Set in the Future About the Past
Mental health needs among inmates and self harm attempts — my new paper just out in JHR
S3E12: Daniel Chen, Political Economy, Toulouse
S3E11: Peter Klein, Entrepreneurship, Baylor
Mixtape Sessions: Past, Present and Future (maybe)
S3E10: Richard Blundell, Labor Economist, University of College London
[Reposting S1E14]: Interview with Petra Todd, Econometrician, University of Pennsylvania
Triple differences part 5: Presenting the event study plots
Another workshop announcement, heading to Houston and desiring a tribe
[Reposting] S1E27: Interview with Kyle Kretschman, head of economics at Spotify
Workshop announcement and getting back on track
Saturday links: Europe edition
Was Neyman's 1923 Potential Outcomes Notation Originally for Continuous Treatments?
S3E9: Pierre Chiappori, Micro Theorist, Columbia University
Generative AI and Worker Productivity
S3E8: Marianne Bitler, Public Economist, UC Davis
Last plug for Demand Estimation Workshop
Another workshop announcement: diff-in-diff
Workshop announcement: Difference-in-differences (Causal 2)
Workshop announcement: DEMAND ESTIMATION
S3E7: Wilbert van der Klaauw, Research Economist, NY Federal Reserve
Mixtape Mailbag #10: Letters from and to a Reader Contemplating Dropping Out of Their PhD Program
Decomposing TWFE in a Continuous Diff-in-Diff: Part 1
Brief mention of continuous diff in diff
S3E6: Bruce Hansen, Econometrician, Univ of Wisconsin
Mixtape Mailbag #9: Log Transformations in Diff-in-Diff with Continuous Treatments
S3E5: Chris Taber, Labor Economist, Wisconsin
Mixtape Mailbag #8: Continuous Triple Differences
Using ChatGPT-4 for a new price discriminating hurdle for my workshops
Difference-in-Differences: No Anticipation with Parallel Trends (with a simulation and equations)
S3E4: Andrew Baker, Professor, UC Berkeley Law
Workshop announcement: Causal Inference 1
Workshop announcement: $95 price for non-tenure track or professors with higher teaching loads
S3E3: Carlos Cinelli, Statistician, University of Washington
Labor's Design and the Making of Good Music
Redesigning my Causal Inference Class and a Sharing of my Worldview
S3E2: Caitlin Myers, Labor Economist, Middlebury College
Mixtape Mailbag #6: Redefining a diff-in-diff problem as a SUTVA violation
Using ChatGPT-4 to Decorate My Living Room
Workshop announcement: Causal I
Scientific Accuracy and Readability
Workshop announcement: demand estimation
S3E1: Richard Freeman, Labor Economist, Harvard
Saturday morning weekly roundup