---
title: "Imagination Exercise"
subtitle: "Module Topic Here"
author: "Your Name Here"
institute: "Old Dominion University"
format: 
  html:
    theme: lux # Check here for more themes: https://quarto.org/docs/output-formats/html-themes.html
    code-tools: true
    code-fold: true
    code-summary: "Code"
    code-copy: hover
    link-external-newwindow: true
    tbl-cap-location: top
    fig-cap-location: bottom

self-contained: true
editor: source
---

```{r setup, include=FALSE}
# DO NOT EDIT THIS

knitr::opts_chunk$set(fig.align = 'center')
knitr::opts_chunk$set(out.width = '90%')
knitr::opts_chunk$set(results = 'hold')
knitr::opts_chunk$set(fig.show = 'hold')
knitr::opts_chunk$set(error = TRUE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message = FALSE)
par(mar = c(4.1, 4.1, 1.1, 4.1))

hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold.", type)]])) return(res)
    
    paste0(
      "<details open><summary>", gsub("^p", "P", gsub("^o", "O", type)), "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}

knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot")
)
```

### Application Description

Describe the application you imagine using the particular method we're studying.  Be sure to include a testable hypothesis and/or an answerable, specific research question. Describe any backdoors that pose a threat to your identification strategy and how you might address them. (**4 Points**)

*Write your answer here.*

### Data Description

Describe the ideal data set for identifying the causal effect of interest described in the application above.  You don’t need to constrain yourself to known, existing variables and data sets, although identifying actual data is OK.  You can also assume you have unlimited ability/resources to measure variables if you want.  Identify the dependent variable in your regression model and the explanatory variables. (**4 Points**)

*Write your answer here.*

### Causal Inference Method Description

Discuss why the method we're studying is suited to the application and data described above.  What assumptions must hold for the method to deliver plausibly causal estimates? (**4 Points**)

*Write your answer here.*

### Expected Results

What is the expected sign of the causal parameter of interest?  Explain what this parameter means in words, in terms of this specific application.  Explain why (or why not) you think that the estimated relationship reflects causality, and not just correlation. (**4 Points**)

*Write your answer here.*

### What Will Be Learned?

What will you learn from this empirical analysis?  Why is this causal relationship important in the context of your application?  How can this evidence be used to inform policy, business decisions, or your own behavior? (**4 Points**)

*Write your answer here.*

### Video Abstract (online students only)

Submit a short (e.g. 3-5 minutes) video pitch of your imagination exercise.  Be sure to cover all of the above topics including (but not limited to) institutional knowledge/background, specific research question, general and specific threats to your identifcation strategy, necessary assumptions, data, etc. (**6 Points**) In addition, provide comments to two other students via the discussion board. Comments may be made up to a week after the due date of the assignment. (**4 Points**)