A law student writes:
I aspire to become a quantitatively equipped/focused legal academic. Despite majoring in economics at college, I feel insufficiently confident in my statistical literacy. Given your publicly available work on learning basic statistical programming, I thought I would reach out to you and ask for advice on understanding modeling and causal inference ab initio. I would be very grateful for any recommendations for books, learning software and whatever else you may think is necessary/helpful.
To start with, I recommend my forthcoming book with Jennifer Hill and Aki Vehtari, Regression and Other Stories, also Richard McElreath’s Statistical Rethinking book as a start on thinking about Bayesian modeling. These books use R and Stan.
These articles might be helpful too:
Causality and statistical learning (from 2011)
Why ask why? Forward causal inference and reverse causal questions (from 2013)
Evidence on the deleterious impact of sustained use of polynomial regression on causal inference (from 2015)
The failure of null hypothesis significance testing when studying incremental changes, and what to do about it (from 2018)
Also, I like the book, A Quantitative Tour of the Social Sciences, edited by Jeronimo Cortina and myself and containing contributions by leading researchers in the fields of history, sociology, economics, political science, and psychology. This is not a statistics book; rather, it gives a sense of the different ways that people in these different fields think about quantitative research.
I’m mostly pointing out stuff written or edited by myself because (a) that’s what I’m most familiar with, and (b) I’m the on you asked! Maybe some commenters will have some suggestions of other good books to read and software to learn.