Assignment - Problem Set

General Guidelines

  • While collaboration with fellow students on the problem set is encouraged, it is crucial that you independently write your own answers. You may ask each other questions and work together, but each student must produce their own written responses.
  • Be sure to review the Academic Integrity Policy in the Syllabus before beginning the assignment.
  • Answers should be clear, well-organized, and legible. Ensure that your responses follow the order of the questions, and include the corresponding question numbers.
  • Although handwritten answers will be accepted, typed mathematical expressions and computer-generated graphs are preferred. If you must submit handwritten answers, please ensure they are easy to read.
  • Students must include a section explicitly detailing how Large Language Models (LLMs) were used to complete the problem set. List all prompts used. If LLMs were not utilized, state, "LLMs were not used in this assignment."
  • Include your Python/R/Stata script as an appendix.
The problem sets will account for 45% of your overall course grade. Your grade for this project will be determined as follows:

Assignments % of Final Grade Due
Problem Set 1
15% October 16
Problem Set 2
15% November 16
Problem Set 3
15% TBD
Topics to be covered
  • Experimental Design
  • Instrumental variable analysis
Topics to be covered
  • Regression Discontinuity
  • Diff-in-Diff
Topics to be covered
  • Machine Learning
  • Factor Analysis