Schedule
In general, on Mondays we will have brief lectures and demonstrations in R, while on Wednesdays, we will have student-led paper discussions and/or replication exercises of published papers. In the latter half of the course, we will shift to work-shopping our own projects and discussing additional issues and considerations with applied causal inference. In this way, we will use a partially flipped classroom and strive to create a collaborative and inclusive classroom environment to discuss, ask questions, collaborate, and get feedback on your analyses.
The tentative schedule of content, subject to change based on student interests and availability of guest speakers, is as follows:
| Monday | Wednesday | Goals/Topics | |
|---|---|---|---|
| Week 1 (1/13) | Course Intro | Intro to causal inference and counterfactual causality | • Intro to course and goals • Understand students’ goals for the course • Set norms and expectations • Intro to concepts in causal inference and motivation for applying it to ecology and evolution • Clarify causal versus other research questions and aims • Brief Introduction to two main frameworks for counterfactual causality (potential outcomes and graphical causal modeling frameworks) |
| Week 2 (1/20) | No class - MLK Day | Intro to the main frameworks for counterfactual causal inference Guest speaker: Dr. Suchinta Arif |
• Structural Causal Models and Directed Acyclic Graphs |
| Week 3 (1/27) | Intro to the main frameworks for counterfactual causal inference | Randomized Controlled Experiments (or RCTs) and experimental design | • Potential outcomes framework • Application of potential outcomes framework to RCTs • Review key assumptions of RCTs • Critique experimental designs in ecology and identify solutions • Dissect experimental design with respect to assumptions required for causal inference |
| Week 4 (2/3) | Observational data and counterfactuals; Introduction to quasi-experimental methods | Creating and Analyzing DAGs (Guest speaker: [Dr. Zach Laubach](https://laubach.github.io)) Due: first draft of research question, or lit review proposal, and DAG analysis on 2/7 |
• Articulate how and why observational data deviates from assumptions of RCTs Project/Workshopping: • Students create and workshop DAGs for their own research/study systems |
| Week 5 (2/10) | Pre-regression matching | Paper discussion and replication exercise | • Learn pre-regression matching as a method • Demo application in R • See & critique how it is applied in the literature |
| Week 6 (2/17) | Difference in Difference (DiD) | Paper discussion and replication exercise | • Learn DiD designs, including identification assumptions and interpretation • Demo application in R • Compare DiD to matching and experiments • See & critique how it is applied in the literature |
| Week 7 (2/24) | Panel methods continued: Two way fixed effects and extensions | Paper discussion and replication exercise - Class on zoom Due Draft of DAG OR literature review proposal (2 page max.) |
• Learn within estimators (two-way fixed effects) including identification assumptions and interpretation • Compare panel designs with conditioning on observables designs and with random effect/mixed effect models in R and understand the differences in assumptions • Compare applications in literature and the assumptions required for the conclusions drawn |
| Week 8 (3/3) | Synthetic Control (Asia Kaiser) | Extensions of diff-in-diff and fixed effects (Dr. Jarrett Brynes) On zoom |
• Learn synthetic control including identification assumptions and interpretation • Compare it to other types of designs |
| Week 9 (3/10) | Instrumental Variables (IV) | IV Paper discussion | • Learn IV as a method • Demo application in R • See & critique how it is applied in the literature |
| Week 10 (3/17) | One-on-one consultations on projects on zoom | RegressionDiscontinuity Designs (RDD) | • Make progress on project and get feedback on proposed research design and challenges so far from Laura and your classmates • Learn RDD as a method |
| Week 11 (3/24) | Spring break (no class) | Spring break (no class) | |
| Week 12 (3/31) | RDD Paper discussion | DUE: In class - brief presentation on project | • Demo application in R • Make progress on project and get feedback on proposed research design and challenges so far from Laura and your classmates |
| Week 13 (4/7) | Project presentations continued; Intro to robustness checks | Comparison of study designs cont’d; Sensitivity tests | • Articulate, compare, and parse the assumptions, estimands, and generalizability of different designs • Intro to Sensitivity Tests |
| Week 14 (4/14) | Mediation Analysis | Power, Inference, and Robust and Clustered Standard Errors | • Introduction to mediation analysis • Articulate additional assumptions for mediation • • Standard errors, power, and inference with quasi-experimental methods |
| Week 15 (4/21) | Special topic: Generalizability (Guest speaker: Dr. Becks Spake) | Project presentations | • • |
| Week 16 (4/28) | Project presentations | May 4th: Final Write up due by email | • Clearly communicate the application of causal inference methods to a research question in ecology OR present a topic we did not cover as a class |
Potential additional topics/units to be voted on by the class:
Generalizability in experimental and observational studies
Heterogeneous treatment effects and conditional average effects
e.g. Causal Forests