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DAGassist in 10 minutes

What you’ll learn

How to go from a dagitty DAG + formula to a compact report
How to interpret the report, and what the variable categories and comparison models mean
How to export to LaTeX / Word / Excel / text

Load packages

library(DAGassist)
# load helper libraries
library(modelsummary)
library(dagitty)

Make your DAG and data

n <- 500
Z <- rnorm(n)
A <- rnorm(n)
X <- 0.6*Z + rnorm(n)
M <- 0.5*X + 0.3*Z + rnorm(n)
Y <- 1.0*X + 0.0*M + 0.5*Z + rnorm(n)
df <- data.frame(Y, X, M, Z, A)

dag_model <- dagitty('
  dag {
    Z -> X
    Z -> Y
    A -> Y
    X -> M
    X -> Y
    M -> Y
    X [exposure]
    Y [outcome]
  }')

Generate a console report with `DAGassist`

See the Getting Started vignette for a guide on using different parameters get the most out of DAGassist. For the purpose of this tutorial, we will keep it simple.

See the Supported Models vignette for documentation on what engines DAGassist supports. Since DAGassist is model-agnostic, if an engine accepts a standard formula + data interface, it will usually work.

Interpreting the output:

ROLES: DAGassist classifies the variables in your formula by causal role, based on the relationships in your DAG. It classifies according to these categories.
- X is the treatment / independent variable / exposure.
- Y is the outcome / dependent variable.
- conf stands for confounder, a common cause of X and Y. Confounders create a spurious association between X and Y, and must be adjusted for.
- med stands for mediator, a variable that lies on a path from X to Y, which transmit some of the effect from X to Y. One should not adjust for mediators if one wants to estimate the total effect of X on Y.
- col stands for collider, a direct common descendant of X and Y. Colliders already block paths, so adjusting for it opens a spurious association between X and Y.
- IO stands for intermediate outcome, a descendant of Y, which introduces bias if adjusted for.
- dMed / Dmediator stands for descendant of a mediator, which should not be adjusted for when estimating total effect.
- dCol / Dcollider stands for descendant of a collider. Adjusting for a descendant of a collider opens a spurious association between X and Y.
- other variables, such as those that only affect the outcome, do not fit any of the previous definitions. They are included in the canonical model because they can be safely included as controls, but are omitted from the minimal model because their inclusion is not strictly necessary. Since other variables’ effects are generally neutral, it is usually best to use the minimal adjustment set as your baseline model.
MODEL COMPARISON:
Minimal is the smallest adjustment set necessary to close all back-door paths from the independent to the dependent variable. The minimal set only includes confounders as controls.
Canonical is the largest permissible adjustment set. Essentially, the canonical set contains all control variables that are not confounders, mediators, intermediate outcomes, descendants of mediatiors, or descendants of colliders.


DAGassist(
  dag = dag_model,
  formula = lm(Y ~ X + M + Z + A, data = df)
)
#> DAGassist Report: 
#> 
#> Roles:
#> variable  role        X  Y  conf  med  col  IO  dMed  dCol
#> X         exposure    x                                   
#> Y         outcome        x                      x         
#> Z         confounder        x                             
#> M         mediator                x                       
#> A         other                                           
#> 
#>  (!) Bad controls in your formula: {M}
#> Minimal controls 1: {Z}
#> Canonical controls: {A, Z}
#> 
#> Formulas:
#>   original:  Y ~ X + M + Z + A
#> 
#> Model comparison:
#> 
#> +---+----------+-----------+-----------+
#> |   | Original | Minimal 1 | Canonical |
#> +===+==========+===========+===========+
#> | X | 1.089*** | 1.091***  | 1.092***  |
#> +---+----------+-----------+-----------+
#> |   | (0.051)  | (0.044)   | (0.044)   |
#> +---+----------+-----------+-----------+
#> | M | 0.007    |           |           |
#> +---+----------+-----------+-----------+
#> |   | (0.044)  |           |           |
#> +---+----------+-----------+-----------+
#> | Z | 0.461*** | 0.463***  | 0.463***  |
#> +---+----------+-----------+-----------+
#> |   | (0.051)  | (0.050)   | (0.050)   |
#> +---+----------+-----------+-----------+
#> | A | 0.020    |           | 0.020     |
#> +---+----------+-----------+-----------+
#> |   | (0.041)  |           | (0.041)   |
#> +===+==========+===========+===========+
#> | + p < 0.1, * p < 0.05, ** p < 0.01,  |
#> | *** p < 0.001                        |
#> +===+==========+===========+===========+

Exporting

See the Making Reports vignette for more detailed information on producing publication-quality DAGassist reports in LaTex, Word, Excel, and plaintext.

Since DAGassist is designed to make appendix robustness checks, this is an example of how to output a report in LaTeX.

#initialize a temporary path
out_tex <- file.path(tempdir(), "dagassist_report.tex")

DAGassist(
  dag = dag_model,
  formula = lm(Y ~ X + M + Z + A, data = df),
  type = "latex", 
  out = out_tex) #put your output directory and file name here

cat(readLines(out_tex, n = 15), sep = "\n") # briefly show the output
#> % ---- DAGassist LaTeX fragment (no preamble) ----
#> % Requires: \usepackage{tabularray} \UseTblrLibrary{booktabs,siunitx,talltblr}
#> \begingroup\footnotesize
#> \begingroup\setlength{\emergencystretch}{3em}
#> \begin{longtblr}[presep=0pt, postsep=0pt, caption={DAGassist Report:}, label={tab:dagassist}]%
#> {width=\textwidth,colsep=1.5pt,rowsep=0pt,abovesep=0pt,belowsep=0pt,colspec={X[35,l]X[15,l]X[8,c]X[8,c]X[8,c]X[8,c]X[8,c]X[8,c]X[8,c]X[8,c]}}
#> \toprule
#> Variable & Role & X & Y & CON & MED & COL & IO & dMed & dCol \\
#> \midrule
#> A & other &  &  &  &  &  &  &  &  \\
#> M & mediator &  &  &  & x &  &  &  &  \\
#> X & exposure & x &  &  &  &  &  &  &  \\
#> Y & outcome &  & x &  &  &  &  & x &  \\
#> Z & confounder &  &  & x &  &  &  &  &  \\
#> \bottomrule