An essay for harmony between Bayesian analysis and causal inference in political science
It probably doesn't work to build it yourself from scratch yet. See 'technical' section in to-do list
Legwork
- WIPs (Hall)
- Decide which interventions (unif, logit)
- Priors and PPCs (?)
- Estimate
- Diagnoses (appendix)
- Posterior figs
- Posterior R inline
- Replace "intro" code with brm (or clean it up, whatever)
- Pooling / Regularization (Reeves et al)
- Which priors
- PPCs into paper somewhere (ugh)
- Estimate
- Diagnosis (appendix)
- Posteriors
- Regularized conjoint (cut)
- Decide if this is gonna make it
Writing
- Feedback solicitation
- What's the audience
- What's the purpose (argument vs. explanation vs. tactical demonstration)
- What needs more careful explanation or (yuck) "hand-holding"
- Intro
- onboarding in the right place?
- Shared goals
- Norm clash
- Examples
- Going forward
- Nonparametrics?
- Bayesian ML tactics?
- Sensitivity testing
- Marginalizing over choices (mediation sensitivity parameter, matching, assumptions)
Reverse outline
- build
- fix
Technical
- Github
- Some kind of build file
- bib isn't self-contained
- font notes
- notes files