Collection of Advice and Resources
This page is my notebook and essentially a living document where I save advice or resources I have come across and found helpful.
On Research Questions
Paul Graham on how to do good worklink to blog post
Advice I have come across
Here I list advice that has been shared with me. If you see your advice listed here and are comfortable with adding your name, I’m happy to turn it into a quote.
On Research Questions & Paper Ideas
- Don’t write a paper somebody else will write within the next six months. This advice reminds me that we serve science best by we find our comparative advantage (be it perspective, approach, methods).
- Work on research questions firms are not going to study. I found this helpful as it reminds me when that an increased reliance of firm data also increases the risk of biasing science since firms will get to decide (to an extend) what questions we are allowed to study.
On Polishing Papers
- Research questions are stronger when 1) the topic interests a general academic audience, not just yourself adn 2) the alternative hypothesis - and not just the main hypothesis - is strong. Don’t just tell us what your contribution is but get buy-in early in the paper on WHY the question is important. Identify the three take-aways of your paper adn make sure everything - from the intro to the tables/graphs is building up to them. ~Anya Samek on Twitter
On navigating Academia
This section lists some resources and insights which helped me “demystify academia” or in other words,
On Metrics
- Applied Causal Inference Powered by ML and AI© ~ by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
“The book introduces ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and presents Debiased Machine Learning methods to do inference in such models using modern predictive tools.”
Organizing Quantitiative Data Projects and Code
CodeAndData.pdf (stanford.edu) Or in HTML format: Code and Data for the Social Sciences:A Practitioner’s Guide (stanford.edu) healthinequality-code/code/readme.md at main · michaelstepner/healthinequality-code (github.com)
On Building a Website
on Website
On Websites... Here are webpages I like and have borrowed from: - Kirby Nielsen - https://www.korykroft.com/ -
MC: https://www.w3schools.com/howto/howto_js_read_more.asp
https://www.w3schools.com/howto/tryit.asp?filename=tryhow_css_text_buttons
On Python
- Python & Econometrics: https://github.com/weijie-chen/Econometrics-With-Python
On PhDing
- NCFDD
This website has webinars and resources available for free to plan research, manage work without burning out. You can register with your utoronto email address and receive a newsletter every monday, which can be helpful to start the week. - Doing research ~ by Paul Niehaus, Nov 22, 2019
Excellent post for students transitioning from coursework to research.
On Writing
- Explorations of Style - A Blog about Academic Writing ~ by Rachael Cayley
This website provides suggestions on how to improve your writing which are precise and actionable. In addition, it offers suggestions on how to stay motivated, become a more productive writer, and how to enjoy writing more. Prof. Cayley also has an excellent book called Thriving as a Graduate Writer .
On Presenting
- Making Slides ~ by Kieran Healy
An essay on how to make slides effective. - href=”https://themockup.blog/posts/2020-09-04-10-table-rules-in-r/#rule-9-group-similar-data-and-increase-white-space”> 10+ Guidelines for Better Tables in R> ~ by Thomas Mock (based on Jon Schwabish)
Great suggestions on how to improve your tables.
On Advising
- Collective feedback from your advisees: https://colinraffel.com/blog/getting-collective-feedback-from-your-advisees.html