00-preface.qmd

# Quick guide {.unnumbered}

```{r, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

source("_setup.R")
```

```{r HJ-F-cover, echo = FALSE, include=identical(knitr:::pandoc_to(), 'html'), fig.align = "center", warning = FALSE, include = FALSE}

m <- make_model("A -> K1 -> B <- K2; B->K3 ; B <-> K3", add_causal_types = FALSE) 


  hj_ggdag(model = m, 
           x = c(1, 2.5, 2, 3, 4.5), 
           y = c(1, 1.25, 1, 1, .75))  +                       
    theme(panel.border = element_rect(color = "#1b98e0",
                                    fill = NA,
                                    size = 3))


```

This book has four main parts:

* Part I introduces causal models and a Bayesian approach to learning about them and drawing inferences from them.

* Part II applies these tools to strategies that use  process tracing, mixed methods, and "model aggregation."

* Part III turns to design decisions, exploring strategies for assessing what kind of data is most useful for addressing different kinds of research questions given knowledge to date about a population or a case.

*  In Part IV we put models into question and outline a range of strategies one can use to justify and evaluate causal models. 

## Resources  {-}

We (with wonderful colleagues) have developed an `R` package---`CausalQueries`---to accompany this book, hosted on [Cran](https://cran.r-project.org/web/packages/CausalQueries/index.html). Supplementary Materials, including a guide to the package, can be found at [https://integrated-inferences.github.io/](https://integrated-inferences.github.io/).
\newpage

## Corrections {-} 

If (when!) we find errors we will correct them using track changes formatting ~~lik htis~~
`r colorize("like this")` and list notable instances in section  @sec-errata.