Malcolm Barrett

Once ggdag 0.3.0 is out, I'll need to overhaul the syntax here. Currently, it's a mix of newer and older ways of doing things. Relatedly, in #243, I found that...

Working on the sensitivity chapter has led me to a bit of a deep dive into using some variables from `touringplans::parks_metadata_raw` to better capture variables related to the crowd flow...

Include ggdag?

1

My package [ggdag](https://cran.r-project.org/web/packages/ggdag/index.html) is a popular extension for dagitty to visualize and analyze causal DAGs. It would be great if you all would consider adding it to this list

``` r library(ggdag, warn.conflicts = FALSE) coffee_cancer_dag ``` r dagitty::dconnected(coffee_cancer_dag, "coffee", "cancer") # [1] TRUE ``` Created on 2024-08-13 with [reprex v2.1.0](https://reprex.tidyverse.org)

Per an accidental finding from CRAN tests, DAGs without edges fail. Kicking the can down the road for the CRAN version but will do this before the next release. ```...

I've had an idea set seed in my mind for the chapter on evidence about *statistical* inference. We can't cover this topic in depth but we can clarify what we're...

Which appears to improve subjective interpretation of inferential results https://www.pnas.org/doi/full/10.1073/pnas.2302491120 Requires a marginalized approach in some capacity for most problems in the book

I'd like to throw in a callout box about how you can take the weighted outcome model from IPW and do g-computation with it if, say, you want to calculate...

https://stefvanbuuren.name/fimd/sec-nonignorable.html#sec:nonignorable https://stefvanbuuren.name/fimd/sec-sensitivity.html

#252 introduces a foundation for discussing missingness and measurement error. I'm happy with this pedagogically, but I think there are a few areas to improve after @LucyMcGowan works on the...