subeniwal / design-based-inference

Design-Based Inference Mixtape Session taught by Peter Hull

About

Many regressions and instrumental variable (IV) specifications can be understood as leveraging the “design” of observed shocks for credibly estimating causal effects or structural parameters. This three-day workshop will build up this design-based toolkit and illustrate some of its advantages over alternative identification strategies. Questions we will seek to answer include:

• "What controls do I need to include to avoid omitted variables bias?"
• "Do I need to worry about ’negative weighting’ of heterogeneous effects?"
• "How should I be clustering my standard errors?"
• "What’s the payoff to considering nonlinear/’structural’ analyses?"

The course will include two programming exercises, where different techniques will be illustrated in real-world applications.

Schedule

This is a three-day (9 hour) intensive workshop, with 6-7 hours of lectures and two 30-minute coding demonstrations. The remaining time will be given to breaks. The coding demonstrations will feature me going through a real-world application, which will be handed out in advance if you’d like to attempt it on your own or in small groups beforehand.

Lecture 1: Selection-on-Observables

Lecture 2: Design vs. Outcome Models

Lecture 3: Design-Based IV

Live-Coding Application 1

Lecture 4: Negative Weights

Lecture 5: Clustering

Live-Coding Application 2

Lecture 6: Recentering

Lecture 7: Nonlinear Models

Readings

Here are selected readings that accompany the course.

Abdulkadiroglu, Angrist, Hull, and Pathak (2016) Angrist and Krueger (1991) Dale and Krueger (2001) Finkelstein (2007)