Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes. Initial versions of the slides (from previous years) may be updated the days preceding the lecture.

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  Lecture 10 - Algorithmic Pricing complications 2
  tl;dr: Algorithmic pricing: capacity, price differentiation, and competition
  [slides]
  
• 

  Lecture 1 - Course Introduction
  tl;dr: Course introduction.
  [slides]
  
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  Lecture 2 - Data challenges
  tl;dr: Common challenges in data collection.
  [slides]
  

  Suggested Readings:

  • Lessons from measurement [only need to read measurement section]
  • When You Hear the Margin of Error Is Plus or Minus 3 Percent, Think 7 Instead
• NO CLASS -- Labor day
  tl;dr:
  
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  Lecture 3 - Survey weighting
  tl;dr: Weighting techniques to counter selection biases.
  [slides]
  

  Suggested Readings:

  • Pew - comparing weighting techniques
  • High-frequency polling with non-representative data
  • We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results.
  • How One 19-Year-Old Illinois Man Is Distorting National Polling Averages
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  Lecture 4 - Weighting 2
  tl;dr: More on weighting, Uncertainty quantification with selection on unknown covariates
  [slides]
  
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  Lecture 5 - Other aspects of data collection
  tl;dr: Other topics in data collection
  [slides]
  

  Optional Readings:

  • Designing Informative Rating Systems: Evidence from an Online Labor Market
  • Reputation Inflation
  • Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
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  Lecture 6 - Recommendations introduction
  tl;dr: Introduction to Recommendations, including data challenges and collaborative filtering
  [slides]
  

  Suggested Readings:

  • Mining Massive Datasets, Ch 9, Recommendations
  • Stitchfix Algorithms tour
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  Lecture 7 - Recommendations, from predictions to decisions
  tl;dr: From predicting ratings to making decisions: capacity constraints and multi-sided recommendations
  [slides]
  

  Suggested Readings:

  • The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
  • Algorithmic Effects on the Diversity of Consumption on Spotify
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  Lecture 8 - Algorithmic Pricing basics
  tl;dr: Introduction to algorithmic Pricing
  [slides]
  
• 

  Special Lecture (by Gabriel, Evan, Sidhika) -- Challenges in real-world data
  tl;dr: Gabriel, Evan, and Sidhika will discuss challenges in real-world data
  
  
• 

  Guest Lecture -- Deb Raji
  tl;dr: Deb Raji (UC Berkeley).
  
  
• 

  Lecture 9 - Algorithmic Pricing complications
  tl;dr: Algorithmic pricing: capacity, price differentiation, and competition
  [slides]
  

  Suggested Readings:

  • The Algorithm behind Plane Ticket Prices and How to Get the Best Deal
  • Book chapter on Dynamic Programming

  Notes:

  • Handwritten notes backward induction 1
  • Handwritten notes backward induction 2
• 

  Lecture 11 - Algorithmic Pricing practice -- ride-hailing
  tl;dr: Algorithmic pricing: ride-hailing case study
  [slides]
  
• NO CLASS -- FALL BREAK
  tl;dr:
  
• 

  Lecture 12 - Experimentation -- Introduction
  tl;dr: Introduction to experimentation
  [slides]
  

  Suggested Readings:

  • Controlled experiments on the web: survey and practical guide
  • A/B testing: A step-by-step guide in Python
  • The Power of Experiments: Decision Making in a Data-Driven World
  • Harvard Business Review on Experimentation
• 

  Lecture 13 - Experimentation -- Peeking and Interference
  tl;dr: Experimentation challenges in online marketplaces: peeking and interference
  [slides]
  

  Suggested Readings:

  • Lyft – experimentation in marketplaces (Part 1), (Part 2), (Part 3)
  • Toward Causal Inference With Interference
• 

  In class discussion - Ethics in (Algorithmic) Pricing
  tl;dr: What makes a pricing decision unfair?
  
  

  Suggested Readings:

  • Fairness as a Constraint on Profit Seeking: Entitlements in the Market
• 

  Guest Lecture -- Allison Koenecke
  tl;dr: Allison Koenecke (Cornell).
  
  

  Title: Equitable Decision-Making in Public Resource Allocation

  Abstract: Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. In this talk, I audit the equity of decision-making in two public resource allocation settings. First, I present a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits – specifically, considering budget-constrained trade-offs between ad conversions for English-speaking and Spanish-speaking SNAP applicants. Second, I discuss sensitivity analyses on public funding allocation algorithms such as CalEnviroScreen, an algorithm used to promote environmental justice by aiding disadvantaged census tracts – which we find to encode bias against tracts with high immigrant populations. In both case studies, we will discuss methods to mitigate allocative harm and to foster equitable outcomes using accountability mechanisms.

  Required reading: CalEnviroScreen

  Optional reading: SNAP,

  Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell University. Her research on algorithmic fairness applies computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health. Koenecke is regularly quoted as an expert on disparities in automated speech-to-text systems. She previously held a postdoctoral researcher role at Microsoft Research and received her PhD from Stanford’s Institute for Computational and Mathematical Engineering. Awards won include the NSF Graduate Research Fellowship and Forbes 30 under 30 in Science.

• CANCELLED CLASS
  tl;dr:
  
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  Guest Lecture -- Roshni Sahoo
  tl;dr: Roshni Sahoo (Stanford).
  
  
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  Special Lecture (by Erica, Kenny, Sophie) -- various aspects of recommenders
  tl;dr: Erica, Kenny, Sophie Sidhika will discuss various aspects of recommenders