-
Notifications
You must be signed in to change notification settings - Fork 1
/
project.Rmd
162 lines (101 loc) · 8.91 KB
/
project.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
```{r project_setup, include=FALSE}
knitr::opts_chunk$set(echo=TRUE, eval=FALSE)
```
# (PART) Project {-}
# Final Project {-}
My sole hope for this project is for it to ***further your goals*** for taking this course. Let's collaborate to make it a good experience for you.
<br><br><br><br>
## Project Options {-}
### Option 1: Data analysis {-}
<!-- Clarify that this can just be a **PLAN** for a data analysis. Ask questions that you are genuinely interested in and describe what data would need to be collected. -->
**Collaboration:** Groups of up to 3. Individual work is fine.
Perform a causal analysis on a dataset of your choice.
- The methods you use for analysis will vary depending on your research questions and the structure of your data (e.g., different methods for quasi-experimental designs vs. general observational studies).
- You'll plan your methods in collaboration with the instructor.
- A key part of all analyses will be sensitivity analyses to assess robustness of results to points of uncertainty in the analysis process.
Resources for finding data:
- [Google Dataset Search](https://datasetsearch.research.google.com/)
- [Harvard Dataverse](https://dataverse.harvard.edu/)
- [Inter-university Consortium for Political and Social Research (ICPSR)](https://www.icpsr.umich.edu/)
- [IPUMS](https://ipums.org/)
- Macalester's librarians can also be a great resource for finding data. Schedule an appointment with them [here](https://www.macalester.edu/library/askus/).
<!-- (From https://www.macalester.edu/seriecenter/sample-syllabus-statements-and-policies/#library-resources) -->
<br><br>
### Option 2: Blog posts {-}
**Collaboration:** Individual only.
Write two blog posts explaining causal inference ideas to a general audience.
- Post 1: A Tour of Causal Inference
- Which of our course ideas resonated most with you?
- Lead the reader through these topics in an engaging and cohesive way.
- Post 2: Pick any media item that has interested you. Write a reaction to it / an analysis of it from a causal inference perspective.
- If you're looking to explore some media, the [Casual Inference podcast](https://casualinfer.libsyn.com/) is a fun one!
<br><br>
### Option 3: Learn an advanced topic {-}
**Collaboration:** Groups of up to 3. Individual work is fine.
Dig deeper into existing course topics or learn a new topic. Examples could include:
- Methods for transportability (generalizability) of effects
- Interference
- Details of methods for time-varying treatments
- Specialized considerations for particular study designs
- Doubly-robust estimation
<br><br>
### Option 4: Other {-}
If none of these options piques your interest, I'm happy to discuss alternatives with you. Some ideas:
- Design a Shiny app to illustrate causal concepts to students
- Write (part of) an R package for making it easier to work with causal graphs
- Critique of applied research
- This is a good option for those who would like to do a data analysis but cannot find adequate data to pursue their question.
- Find and read papers that study a question of interest to you. Critique these papers from a causal inference lens. This will involve constructing your own causal graph to guide a critique of the authors' data collection and analysis methods.
- Discuss what remains uncertain in this line of research, and propose an analysis plan for a new causal study to rectify the limitations of prior research.
<br><br><br><br>
## Deliverables {-}
There is a lot of flexibility in the form that your project takes. Examples include:
- A video presentation
- A podcast-style recording
- A set of blog posts
- A project webpage on a personal website
Work with the instructor to determine the most suitable deliverable for your project, depending on the option you pick.
<br><br><br><br>
## Timeline {-}
- **Milestone 1: Project Proposal** Choose your final project option, topic, and group by Friday, March 10 at midnight CST.
- Data analysis option: Find a dataset and formulate causal question(s) that you want to answer.
- Blog post option: Outline ideas for one post
- Advanced topic option: Settle on an advanced topic, and do some preliminary research to identify key ideas within this topic.
<br>
- **Milestone 2:** Friday, March 31
- Data analysis option: Understand your data context well and construct an initial causal graph.
- Blog post option: Brainstorm ideas or create an outline for one post.
- Advanced topic option: Make progress in learning about one sub-area for your topic.
- Other: Make progress appropriate to the scope of the project.
<br>
- **Milestone 3:** Sunday, April 9 (Check in in-person or via an email exchange)
- Data analysis option: Finalize your causal graph and construct visualizations to inform an appropriate outcome regression and IPW analysis.
- Finalizing your causal graph does not require, for example, doing causal discovery by hand. You should think through your graph holistically along the lines of the flow discussed in the [Drawing Causal Diagrams](https://theeffectbook.net/ch-DrawingCausalDiagrams.html) chapter of The Effect (the reading from HW4). You should feel satisfied that you have represented all relevant variables and the relationships between them.
- Blog post option: Draft Post 1 (A tour of causal inference)
- Advanced topic option: Make progress in learning about one sub-area for your topic.
- Other: Make progress appropriate to the scope of the project.
<br>
- **Milestone 4:** Schedule a time to meet with me during class time this week via [Calendly](https://calendly.com/lmyint). (You can choose an in-person or Zoom option on Calendly.)
- Data analysis option: Based on your visualizations, construct an outcome regression model, and conduct an IPW analysis. Refer to your workflow write-up from HW4 to guide the steps taken here. Organize information on causal effect estimates (overall, and possibly within subgroups, if that is of interest) and confidence intervals.
- Blog post option: Make substantial progress on Post 1 (A tour of causal inference)
- Advanced topic option: Make progress in learning about one sub-area for your topic.
- Other: Make progress appropriate to the scope of the project.
<br>
- **Milestone 5:** Schedule a time to meet with me this week via [Calendly](https://calendly.com/lmyint) to share updates on your progress. (You can choose an in-person or Zoom option on Calendly.)
- Data analysis option: Conduct a sensitivity analysis for unmeasured confounding for your project, and complete a draft of your deliverable.
- Blog post option: Finalize Post 1 (A tour of causal inference) and make substantial progress on Post 2.
- Advanced topic option: Make progress on the goals you set with the instructor in the previous week, and complete a draft of your deliverable.
- Other: Make progress appropriate to the scope of the project, and complete a draft of your deliverable.
<!-- <br>
- **Milestone 3:** Thursday, October 15 or Friday, October 16. Schedule a discussion time with me on [Google Calendar](https://calendar.google.com/calendar/selfsched?sstoken=UUFBWjVpaTNFb2JUfGRlZmF1bHR8ODFhZGU4ZWQ5MjJkOWY2M2Q4YjZhNTgwN2IyMmFlM2Y).
- Data analysis option: Come prepared to tell me about the models you've fit and your interpretations. Share a draft of your artifact so far with me 24 hours in advance.
- Blog post option: Complete full drafts of both posts and share with me 24 hours before our meeting so that I can give feedback and discuss with you.
- Advanced topic option: Come prepared to tell me more about new material that is the equivalent of the second 5 minutes of a 15 minute presentation. Share a draft of your artifact so far with me 24 hours in advance.
<br>
- **Milestone 4:** Monday, October 19 or Tuesday, October 20. Schedule a discussion time with me on [Google Calendar](https://calendar.google.com/calendar/selfsched?sstoken=UUFBWjVpaTNFb2JUfGRlZmF1bHR8ODFhZGU4ZWQ5MjJkOWY2M2Q4YjZhNTgwN2IyMmFlM2Y).
- Data analysis option: Come prepared to discuss final points of feedback from the previous meeting (deadline 3). Finalize your artifact draft, and share with me 24 hours in advance.
- Blog post option: Act on feedback from the previous meeting (deadline 3), and share with me 24 hours before our meeting.
- Advanced topic option: Come prepared to tell me more about new material that is the equivalent of the last 5 minutes of a 15 minute presentation. Finalize your artifact draft, and share with me 24 hours in advance.
<br>
- **Final deadline:** Friday, October 23. By midnight CST, submit the final version of your digital artifact [on Moodle](https://moodle.macalester.edu/mod/assign/view.php?id=39212).
- We will not have a showcase/presentation day in class, but instead, I look forward to seeing you showcase your work for the department at either of our two departmental capstone unveiling days! (One will be at the end of Module 2 and the other at the end of Module 4.) -->