%%{init: {"flowchart": {"htmlLabels": false}} }%%
flowchart LR
A(The book at Cambridge) === B
B(Open access preprint) === C
C("CausalQueries") === D
D("Guide to CausalQueries")
click A "https://www.cambridge.org/core/books/integrated-inferences/45B07964AD4718A74CDE3E35A31F26FA"
click B "https://macartan.github.io/integrated_inferences/"
click C "https://integrated-inferences.github.io/CausalQueries/"
click D "https://macartan.github.io/assets/pdf/papers/2024_CausalQueries.pdf"
Integrated Inferences
Causal Models for Qualitative and Mixed-Method Research
Integrated Inferences provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two.
You can buy the book at Cambridge University Press, read the open access preprint, explore the R package CausalQueries, and read a guide to forming, updating, and querying causal models with CausalQueries (Tietz et al 2024)
