Assignments & Evaluation

Overview

As a 3-credit graduate course, our expectation is that you are here to learn, which involves following along with the course through doing the reading and attending class meetings. We are less interested in grades and more interested in providing a venue to hold you accountable for learning the material. We will provide many opportunities for feedback and workshopping, so completing your assignments and making progress on your project will benefit your learning (and hopefully contribute to your progress on your thesis/dissertation). As such, attendance and participation is 25%, leading of paper discussion/presentation is 25%, and the final report and presentation are worth the remaining 50%.

Main assignment

The main assignment will be either to complete and write up a data analysis or to do a literature review and deep dive on a particular subtopic (ideas listed below). The project or literature review will culminate in a presentation the last week of classes and a final written report.

Final Project Option 1: Data analysis project

For the final project that takes the form of a data analysis, each student will complete a project according to the following process: identifying a research question, creating a DAG, choosing and applying a data analysis, and writing up a methods and results section that 1) articulates the causal relationship of interest, 2) describes the causal identification strategy and choice of study design/methods, 3) describes the implicit assumptions on which this strategy rests, and 4) interprets the results and their potential biases. We encourage students to base this analysis on their thesis research to get the most out of the class.

Final Project Option 2: Literature review

Alternatively, students can complete a literature review that provides a deeper dive on a topic we do not cover in detail in this course (*some ideas are listed below). This direction may be more appropriate for students who are not at a stage of performing analyses for their thesis, who do not have a relevant project that is tackling an empirical causal question, or who are interested in learning more about a topic not covered under our guidance.

Final project presentation

Guidelines for all presentations:

8 minute presentation + 2 minutes for questions
Introduce the question you address and the motivation for your project

Specific guidelines for data analysis project presentations:

Describe the causal inference approach you took and why you chose that approach
Present your DAG
Briefly describe the data you used
Share your results AND contextualize them with the important limitations and assumptions of the method(s) you used
Outline possible next steps you could take to make your analysis more robust in the future

Specific guidelines for literature review presentations:

Describe the challenge for causal inference
Describe and critique the current approaches people are taking
Present your ideas for how the field could better incorporate causal inference methods

Final project write up

For data analysis projects: ~ 5 pages (plus figures, but don’t stress over the page count)
For literature review projects: ~ 10 pages, but don’t stress over the page count – focus on what you need to include to write a compelling paper

Additional assignments

In addition to the project/literature review, each student will also lead a paper(s) discussion during the semester, which will be either a discussion of a paper describing or applying a study design or a critique of a paper aiming to address a causal question. When leading the paper discussions, we recommend starting off with drawing a diagram of the causal relationships in question in the paper.

Readings

The course reading list will include journal articles and book chapters that we will make available to you on our course website. Throughout the semester, the class will compile a running glossary of “jargon” and terms and concepts in causal inference to provide notes as reference. We (the instructors) will provide a bibliography of additional readings and coding resources on various topics for students as reference.

The readings differ in their depth and use of math and notation. You do not need to understand all the equations and notation to gain intuition about the material and concepts. Try to learn it but try not to be too stressed by it.

Course glossary

As noted above, as a class we will compile a running glossary of “jargon” and terms and concepts in causal inference to provide notes as reference.

Glossary assignments:

Week 2: Tyler and Hilary
Week 3: John S.
Week 4: Sam
Week 5: Casey and Aly
Week 6: Katie
Week 7: Alec and Henry
Week 8:
Week 9: Tom and Brendan H.
Week 10: Max
Week 12: Meghan H.

Suggestions for literature review topics (Final project option 2)

You are not limited to these topics, but here are some ideas:

Challenges and solutions for experimental designs and interpretation (choose one):
- Non-compliance and attrition (this is a rich area!)
- Interference between experimental units and spillovers (G&G Ch8)
- Partial identification (excludability violations)
- Mechanisms and mediation analysis (G&G Ch 10)
- Limits to what experiments tell us about heterogenous treatment effects
- Integration of experimental findings (e.g., meta-analyses and extrapolation)
New issues for difference-in-difference and two-way fixed effects estimators (e.g., staggered treatments and heterogeneity
Synthetic control methods (https://mixtape.scunning.com/10-synthetic_control)
Machine learning and causal inference
Sensitivity analyses and/or placebo designs
Weighted regression (extension of matching ideas)
Mechanism (Mediation) Analysis; Sequential G estimation
Remote sensing and causal inference
Systematic measurement error
Causal identification in time series
Inferences in observational designs and networks
Meta-analyses using observational data

Deadlines

Assignment	Deadline
Pre-course survey	Friday 1/20
Students create and workshop DAGs for their own research/study. Come to class prepared to present and share/discuss your DAG	Wednesday 2/1
Draft of DAG OR literature review proposal (1 page max)	Friday 4/14 (by email)
Short presentation on research questions + DAG OR literature review topic, its significance, + some potential applications	Monday 4/17 (in class)
Revised DAG + proposed methods OR literature review outline	Friday 4/21
Presentation of final project	Monday 5/1 and Wednesday 5/3 (in class)
Final project write-up	Friday 5/5