Readings by Class & Additional Materials
Note: book chapters can be found on the private course Google Drive, as well as pdfs of the papers. Demos will be in RMarkdown and on the course GitHub page in the exercises folder.
Due Mon. 1/13
First class; Complete pre-course survey in class; Review Syllabus and website
Due Wed. 1/15 Introduction Continued
- Hernan et al. 2019
- Angrist & Pischke 2008, Chapter 1 (from Mostly Harmless Econometrics)
- Gerber & Green, Chapter 1 (you can just skim this one)
Due Mon. 1/20 - No Class MLK day
Due Wed. 1/22 Structural Causal Model and Directed Acyclic Graphs (DAG)
Guest speaker: Dr. Suchinta Arif
1) Arif & MacNeil 2022
2) Laubach et al. 2021
Optional; additional background reading on DAGs:
3) The Effect Ch. 6
4) Morgan & Winship (2007) pgs. 29-34 and Ch. 3
5) Cunningham (2021) Ch. 3
## Due Mon. 1/27 Potential Outcomes 1) Angrist & Pischke 2008, Chapter 2 (from Mostly Harmless Econometrics)
Optional: Morgan & Winship Book, Ch. 2
Due Wed. 1/29 Experiments and Randomization
- Gerber & Green, Chapter 2
- Kimmel et al. 2021
Optional: Arif & Massey (2023) Reducing bias in experimental ecology through directed acyclic graphs
Due Mon. 2/3 Introduction to Challenges of Observational Data
- Watch Imbens video: 2022 Nobel Prize lecture
- Larsen et al. 2019 - up to section 8, and the Discussion. Can skim in between for preview of what’s to come.
- Siegel & Dee (2025) Ecology Letters - up until section 5.1.
Optional and more advanced: Angrist & Pischke 2008, Chapter 3 (from Mostly Harmless Econometrics)
Optional: - Ferraro 2009
- Ferraro and Hanauer 2014 Advances in Measuring the Environmental and Social Impacts of Environmental Programs
Due Wed. 2/5 - Start your DAGs with Guest Speaker and visitor: Dr. Zach Laubach
- Rohrer 2018
- Review Laubach et al. 2021
- Vanderwheele 2019 Principles of confounder selection
- Review DAG resources and DAG software on the website’s DAGs resources tab
Prior to and during the class period, you will start your projects and make a first DAG. Review the DAG resources page tab on the website, and send Laura your DAG for feedback and dicussion with the class. Be prepared to present and discuss your DAG.
Due Mon. 2/10 Matching (and weighting)
Optional: -Chesnaye et al. 2022
-McCaffery et al. 2013 A Tutorial on Propensity Score Estimation for Multiple Treatments Using Generalized Boosted Models
Due Wed. 2/12 [Discussion lead(s): Miles & Teresea]
demo RMarkdowns from Siegel & Dee (2025) Ecology Letters for weighting and matching on course GitHub here
Due Mon. 2/17 Difference-in-Difference
- Angrist & Pischke 2015, Chapter 5 (note: this is in the book Mastering ’Metrics)
- The Effect Ch. 16 - Fixed Effects up until 16.2.2 Multiple Sets of Fixed Effects.
- The Effect Ch. 18 up until 18.2.5 Rollout Designs and Multiple Treatment Periods.
Due Wed. 2/19 [Discussion lead(s):Advyth + Kathryn ]
DUE Draft of Revised DAG OR literature review proposal (1 page max.)
1) Simler-Williamson & Germino 2022
demo RMarkdown using Boulder Open Space fire and vegetation case on course GitHub (data in GoogleDrive)
Due Mon. 2/24 Panel methods continued; Two-way fixed effects
- Angrist & Pischke 2008, Chapter 5
- The Effect Ch. 16 - Fixed Effects starting at 16.2.2 Multiple Sets of Fixed Effects
Optional: Bell et al. 2019. Fixed and random effects models making an informed choice
Brynes and Dee (2025) Ecology Letters
Due Wed. 2/26 - Fixed Effects Discussion [Hunter + Treson]
Optional (but good for the discussants!) Meehan et al. 2011 vs. Larsen 2013
demo Dee et al. Rmarkdown on course GitHub
Due Mon. 3/3 Synthetic Control (Asia Kaiser)
Optional: The Effect section 21.2.1 Synthetic Control
For a more complete treatment of synthetic control, see: Mixtape Ch. 10
Applications: - Wu et al. 2023
- Lawson and Smith 2023
Due Wed. 3/5 DiD extensions and issues
- Brynes and Dee (2025) Ecology Letters
- The Effect Ch. 18 starting at 18.2.5 Rollout Designs and Multiple Treatment Periods.
demo Brynes & Dee Rmarkdown on [GitHub here] (https://github.com/jebyrnes/ovb_yeah_you_know_me/tree/v1.0.2)
Optional: 1) Roth et al. (2023). What’s trending in difference in difference? 2) Pedro Sant’Anna’s DiD resources and Comprehensive Course on DiD 3)Bell et al. 2019. Fixed and random effects models making an informed choice
Due Mon. 3/10 Instrumental Variables
- Angrist & Pischke 2015, Chapter 3 (note: this is in Mastering ’Metrics)
- Kendall book chapter (in GDrive)
Due Wed. 3/12 Instrumental Variables Applications [Discussion lead(s): Hope & Katie]
Due Mon. 3/17 Project Consultations
1:1 consultations with Laura in lieu of class. Please read the rest of the following papers and come to the call with a proposal for the statistical design you’ll use and reasoning why: Siegel & Dee (2025) Ecology Letters - up until section 5.1. Larsen et al. 2019 - up to section 8, and the Discussion. Can skim in between for preview of what’s to come. 3)
Schedule to sign up here
Due Wed. 3/19 Regression Discontinuity Designs
- The Effect Ch. 20 Optional: Angrist & Pischke 2015, Chapter 4 (note: this is in Mastering ’Metrics in the GDrive)
Week of March 24th - Spring Break, No Classes this week
Due Monday 3/31 RDD Applications [Discussion lead(s): Anna & Jiacheng]
- Englander 2019
- Noack et al. 2022
Optional paper: Burgess et al. 2019
Due Wed. 4/2 First Project Presentations
Brief presentation (5 minutes) of your project: the “so what” big picture, the question you are addressing, its applications, your DAG and/or data context, and your proposed method for feedback and Q&A.
Due Mon. 4/7 Presentations Continued; intro to robustness checks
Due Wed. 4/9 Comparison of Designs [Discussion lead(s): Rachel & Luis ] & Sensitivity Tests (Laura)
Revisit these papers in light of the assumptions of different designs, their different estimands, generalizability, and their assumptions: - Simler-Williamson & Germino 2022
- Dee et al., 2023
- For Sensitivity Tests, see sensemakr help for a primer in R
Optional to revisit for methods comparison 1) Siegel & Dee (2025) Ecology Letters
2) Butsic et al. 2017
3) Arif & MacNiel 2021 Ecosphere
Due Mon. 4/14 Mediation Analysis [Sarah Elizabeth]
Read Correia et al. 2025
Due Wed. 4/16 Power, standard error estimation, and inference [Mobeen and Lincoln]
Read The Effect: Chapter 13 “Your standard errors are probably wrong”
Read Kimmel, Avolio & Ferraro 2023
For a deeper dive on standard error estimation, read Cameron and Miller. A pracitioner’s guide to clustered robust standard errrors
Due Mon. 4/21 Dr. Rebecca Spakes: Generalizability
Read Spake et al. 2022
Due Wed 4/23 Project Presentations
Due Mon. 4/28 Project Presentations
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 4/30 No Class: Work on final report
Projects Due Sunday May 4 by email
See Assignments & Evaluation tab for more information
Recommended Textbooks (not required)
I do not expect you to buy any/all of these books. I will provide the sections that are assigned for class. But if you want to explore topics of causal inference further, I 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.
- Huntinton-Klein, N. The Effect free online
- 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).
*Ding, P. A First Course in Causal Inference. online.
- Holland 1986, JASA
Do you feel like you could use more background or a refresher on the basics?
Variables, distributions, conditional distributins and means, types of data
Hypothesis testing, type I and type II errors
Logistic Regression
Running List of Papers that review best practices for some techniques
Austin & Stuart (2015) for ITPW: Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies.
Kimmel et al. 2023 for power analysis and reporting for reproducibility: Particularly, see suggestions in Table 1: Changes in research practices to help increase the reliability of ecological research.