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 workshopping 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, is as follows:
| Monday | Wednesday | Goals/Topics | |
|---|---|---|---|
| Week 1 (1/16) | No class | Course intro | • Intro to course and goals • Understand students’ goals for the course • Set norms and expectations |
| Week 2 (1/23) | Intro to causal inference and counterfactual causality | Intro to the main frameworks for counterfactual causal inference | • Intro to concepts in causal inference and motivation for applying it to ecology and evolution • Introduction to two main frameworks for counterfactual causality (potential outcomes and graphical causal modeling frameworks) |
| Week 3 (1/30) | Intro to DAGs (Guest speaker: Dr. Zach Laubach) | Workshopping DAGs: come prepared to present and share/discuss your DAG | • DAGs as a tool from the graphical causal modeling framework • Students create and workshop DAGs for their own research/study systems |
| Week 4 (2/6) | Randomized Controlled Experiments (or RCTs) and experimental design | Dissect experimental design with respect to assumptions required for causal inference | • Application of potential outcomes framework to RCTs • Review key assumptions of RCTs • Critique experimental designs in ecology and identify solutions |
| Week 5 (2/13) | Observational data and counterfactuals | Introduction to quasi-experimental methods | • Articulate how and why observational data deviates from assumptions of RCTs • Understand challenges of applying causal inference to observational data, including confounding and selection bias • Get a sense of the landscape of tools for overcoming challenges |
| Week 6 (2/20) | 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 7 (2/27) | Difference in Difference (DiD) | Paper discussion | • 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 8 (3/6) | Panel methods | Paper discussion and replication exercise | • 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 10 (3/13) | Instrumental Variables | Paper discussion | • Learn IV as a method • Demo application in R • See & critique how it is applied in the literature |
| Week 9 (3/20) | Regression Discontinuity Designs (RDD) | Paper discussion | • Learn RDD as a method • Demo application in R • See & critique how it is applied in the literature |
| Week 11 (3/27) | Spring break (no class) | Spring break (no class) | |
| Week 12 (4/3) | Project work session (in groups) | One-on-one consultations on projects | • Make progress on project and get feedback from class |
| Week 13 (4/10) | Project work session | Project work session | • Make progress on project and get feedback on proposed research design and challenges so far |
| Week 14 (4/17) | Brief presentation on project | Project work and workshopping | • Make progress on project and get feedback from class |
| Week 15 (4/24) | Project work and workshopping | Special topic: Generalizability (Guest speaker: Dr. Becks Spake) | • Make progress on project and get feedback from class • Introduction to challenges with generalizability |
| Week 16 (5/1) | Project presentations | Project presentations | • 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 |