Readings & Additional Materials
Recommended Textbooks (not required)
We do not expect you to buy any/all of these books. We will provide the sections that are assigned for class. But if you want to explore topics of causal inference further, we recommend these books!
Note: Some of these texts use examples to illustrate their points that are problematic (e.g., treating gender as a binary or studying post-colonial economic development without considering the violence of colonialism). We do not agree with the assumptions underlying these examples and we acknowledge the problems with them. The descriptions of methods are still some of the easiest to read out there.
- Morgan, SL and C Winship. 2007. Counterfactuals and Causal Inference: methods and principles for social research.
- Gerber, AS and DP Green. 2012. Field Experiments: design, analysis and interpretation. (especially useful if you plan to do field experiments in your research)
- Cunningham, S. 2021. Causal Inference: The Mixtape (New Haven, CT: Yale University Press). Available online for free
- Angrist, JD and JS Pischke. 2015. Mastering ’Metrics: The Path from Cause to Effect (Princeton, NJ: Princeton University Press).
- Angrist, JD and JS Pischke. 2008. Mostly Harmless Econometrics: an empiricist’s companion. (Princeton, NJ: Princeton University Press).
- Rosenbaum, P. 2010. Observational Studies. Springer.
- Pearl, J. and D. Mackenzie. 2018. The Book of Why. (New York, NY: Basic Books, Inc.) (A more popular science book)
- For more technical discussions of causality:
- Holland 1986, JASA
- Heckman 2000, QJE
- Pearl, J. 2009. Causality. (Cambridge, UK: Cambridge University Press).
- Holland 1986, JASA
Due Mon. 1/23
- Hernan et al. 2019
- Angrist & Pischke 2008, Chapter 1
- Gerber & Green, Chapter 1 (you can just skim this one)
Due Wed. 1/25
- Angrist & Pischke 2008, Chapter 2
- Hernan 2016
Due Mon. 1/30
Guest speaker: Dr. Zach Laubach
Due Wed. 2/1
Come to class prepared to present and discuss your DAG.
- Arif & MacNeil 2022
- Watch Hernan video: “3. Elements of DAGs” on Canvas
- Check out DAG software
Optional
For additional background on DAGs:
- Morgan & Winship (2007) pgs. 29-34 and Ch. 3 - see Canvas
- Cunningham (2021) Ch. 3
Other software for making DAGs:
a) ggdag is a nice R package based on dagitty but tidyverse-compatible and with much better plotting functionality
b) shinydag is another GUI aimed at visualizing DAGs and exporting them in different publication-ready formats
c) TETRAD
d) DAG program
e) dagR
Due Mon. 2/6
- Gerber & Green, Chapter 2
- Kimmel et al. 2021
Due Wed. 2/8 [Casey]
- No readings today!
Due Mon. 2/13
- Watch Imbens video: 2022 Nobel Prize lecture
- Ferraro 2009
Due Wed. 2/15 [Brendan & Henry]
Due Mon. 2/20
Due Wed. 2/22 [Alec & Sam]
- Siegel et al. 2022
- Xu et al. 2022
- Siegel et al., submitted (available on Canvas)
Due Mon. 2/27
- Angrist & Pischke 2015, Chapter 5 (note: this is in Mastering ’Metrics)
Due Wed. 3/1 [Anna & Hilary]
Due Mon. 3/6
- Angrist & Pischke 2008, Chapter 5
Due Wed. 3/8 [Meghan & Tom]
- Dee et al., in press, and RMarkdown
- Byrnes & Dee, in prep and RShiny
Optional
- Meehan et al. 2011 vs. Larsen 2013
- Grace et al. 2016 vs. Dee et al., in press
- Dudney et al. 2021
Due Mon. 3/13
- Angrist & Pischke 2015, Chapter 3 (note: this is in Mastering ’Metrics)
- Kendall book chapter (on Canvas)
Due Wed. 3/15 [Max & Tyler]
Due Mon. 3/20
- Angrist & Pischke 2015, Chapter 4 (note: this is in Mastering ’Metrics)
Due Wed. 3/22 [Aly & John]
Optional additional paper: Burgess et al. 2019
Due Mon. 4/3
No readings - come prepared with your causal question to begin your project. Check your email for a zoom link for the class.
In class, start your projects. We have provided a template to provide some structure and facilitate your small group discussions.
Due Wed. 4/5
1:1 consultations with Laura and Katherine in lieu of class. Schedule to sign up here
Due Mon. 4/10
No readings - Presenting or discussing your project question and DAG for feedback
Due Wed. 4/12
No readings - Presenting or discussing your project question and DAG for feedback
Due Fri. 4/14
Draft of DAG OR literature review proposal (1 page max.)
Due Mon. 4/17
Brief presentation (3-5 minutes) of your project: the question you are addressing, its applications, your DAG (if applicable) and your proposed method for feedback and Q&A
Due Wed. 4/19
No assignment – continue working on projects
Fri. 4/21
Revised DAG + proposed methods OR outline of literature review due
Due Mon. 4/24
No assignment – continue working on projects
Due Wed. 4/26
Generalizability in experimental and observational studies
Visit from Dr. Rebecca Spake
Optional
Due Mon. 5/1
Project Presentations
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
Due Wed. 5/3
Project presentations, continued
Fri. 5/5
Final project write-ups due (see Assignments tab for descriptions of the requirements for the write-up)
Potential additional units
Generalizability in experimental and observational studies
Heterogeneous treatment effects
Replication/Reproducibility/Pre-registration
- Kimmel et al., in press