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Getting the Most out of DAGassist Using Parameters

Introduction

DAGassist() is meant to be simple and easy to use, and most of its features can be enjoyed via a simple two-parameter argument:

library(DAGassist)
library(dagitty)

DAGassist(
  dag = your_dag_model,
  formula = your_regression_call
)

But it also offers several parameters for more specific applications. They control how the DAG is evaluated (imply, eval_all), how results print (show, labels, omit_factors, omit_intercept, verbose), which modeling engine to use (engine, engine_args), and which output format to write (type, out). This vignette walks through each with small examples.

Core Arguments

dag and formula

formula can be a standard formula + data regression call, from which DAGassist will impute the necessary information, or three separate formula, data, and engine arguments.

#imputed formula
DAGassist(
  #implies the exposure and outcome from the dagitty object
  dag = dag_model, 
  #implies the engine, formula, and data from the regression call
  formula = lm(Y ~ X + C, data=df) 
)

#plain formula
DAGassist(
  dag = dag_model,
  engine = stats::lm, #stats::lm is the default engine arg
  formula = Y ~ X + C,
  data = df,
  exposure = "X",
  outcome = "Y"
)

The two formulas above will print identical output.

Scope Flags

imply: evaluate on only mentioned variables vs the full DAG

#pruned-to-formula DAG
DAGassist(dag = dag_model, formula = Y ~ X + C, data = df, imply = FALSE, show = "roles")
#> DAGassist Report: 
#> 
#> Roles:
#> variable  role       Exp.  Out.  conf  med  col  dOut  dMed  dCol  dConfOn  dConfOff  NCT  NCO
#> X         exposure   x                                                                        
#> Y         outcome          x                                                                  
#> C         collider                          x    x                                            
#> 
#>  (!) Bad controls in your formula: {C}
#> 
#> Roles legend: Exp. = exposure; Out. = outcome; CON = confounder; MED = mediator; COL = collider; dOut = descendant of outcome; dMed  = descendant of mediator; dCol = descendant of collider; dConfOn = descendant of a confounder on a back-door path; dConfOff = descendant of a confounder off a back-door path; NCT = neutral control on treatment; NCO = neutral control on outcome

#full-DAG evaluation
DAGassist(dag = dag_model, formula = Y ~ X + C, data = df, imply = TRUE,  show = "roles")
#> DAGassist Report: 
#> 
#> Roles:
#> variable  role        Exp.  Out.  conf  med  col  dOut  dMed  dCol  dConfOn  dConfOff  NCT  NCO
#> X         exposure    x                                                                        
#> Y         outcome           x                                                                  
#> Z         confounder              x                                                            
#> M         mediator                      x                                                      
#> C         collider                           x    x     x                                      
#> A         nco                                                                               x  
#> B         nco                                                                               x  
#> 
#>  (!) Bad controls in your formula: {C}
#> 
#> Roles legend: Exp. = exposure; Out. = outcome; CON = confounder; MED = mediator; COL = collider; dOut = descendant of outcome; dMed  = descendant of mediator; dCol = descendant of collider; dConfOn = descendant of a confounder on a back-door path; dConfOff = descendant of a confounder off a back-door path; NCT = neutral control on treatment; NCO = neutral control on outcome

eval_all: keep non-DAG RHS terms in derived models

Sometimes your RHS has terms that aren’t DAG nodes (e.g., fixed effects via i(region), factor expansions, interactions, splines). eval_all decides whether these non-DAG terms are kept in minimal/canonical formulas. - eval_all = FALSE (default): drop RHS terms not present as DAG nodes from the derived formulas. - eval_all = TRUE: keep all original RHS terms that aren’t DAG nodes (e.g., fixed effects), in addition to the DAG-based controls.

DAGassist(
    dag = dag_model,
    formula = fixest::feols(Y ~ X + C + fixest::i(region), data = df),
    imply = TRUE,
    eval_all = TRUE
    )

Display and Labeling

show: sub-reports

# just the roles table
DAGassist(dag = dag_model, formula = Y ~ X + Z + C, data = df, show = "roles")
#just the model comparison
DAGassist(dag = dag_model, formula = Y ~ X + Z + C, data = df, show = "models")

labels: human-readable names

Provide a named character vector or a small data frame. Note that the label parameter uses modelsummary() coef_rename logic, so an incomplete label list will not throw any errors.

labs <- list(
  X = "Exposure",
  Y = "Outcome",
  C = "Collider"
)

DAGassist(
  dag = dag_model, formula = lm(Y ~ X + C, data = df),
  show = "roles", labels = labs
)
#> DAGassist Report: 
#> 
#> Roles:
#> variable  role       Exp.  Out.  conf  med  col  dOut  dMed  dCol  dConfOn  dConfOff  NCT  NCO
#> Exposure  exposure   x                                                                        
#> Outcome   outcome          x                                                                  
#> Collider  collider                          x    x                                            
#> 
#>  (!) Bad controls in your formula: {C}
#> 
#> Roles legend: Exp. = exposure; Out. = outcome; CON = confounder; MED = mediator; COL = collider; dOut = descendant of outcome; dMed  = descendant of mediator; dCol = descendant of collider; dConfOn = descendant of a confounder on a back-door path; dConfOff = descendant of a confounder off a back-door path; NCT = neutral control on treatment; NCO = neutral control on outcome

omit_intercept and omit_factors: output-only filters

These flags only suppress rows in the printed model comparison. They do not remove terms from estimation. omit_factors in particular is useful for conserving space in your report, as reports with factors included can be hundreds of rows.

DAGassist(
    dag = dag_model,
    formula = fixest::feols(Y ~ X + Z + i(region), data = df),
    omit_intercept = TRUE, omit_factors = TRUE # both TRUE by default
  )

bivariate: include a no-covariate comparison column

Include a Y ~ X column for readers who want the raw association. bivariate = FALSE by default.

DAGassist(
  dag = dag_model, 
  formula = lm(Y ~ X + C, data = df),
  show = "models",
  bivariate = TRUE
)
#> DAGassist Report: 
#> 
#> Model comparison:
#> 
#> +---+----------+-----------+-----------+-----------+
#> |   | Original | Bivariate | Minimal 1 | Canonical |
#> +===+==========+===========+===========+===========+
#> | X | 0.908*** | 1.415***  | 1.415***  | 1.415***  |
#> +---+----------+-----------+-----------+-----------+
#> |   | (0.030)  | (0.021)   | (0.021)   | (0.021)   |
#> +---+----------+-----------+-----------+-----------+
#> | C | 0.475*** |           |           |           |
#> +---+----------+-----------+-----------+-----------+
#> |   | (0.022)  |           |           |           |
#> +===+==========+===========+===========+===========+
#> | + p < 0.1, * p < 0.05, ** p < 0.01, *** p <      |
#> | 0.001                                            |
#> +===+==========+===========+===========+===========+

verbose: printing formulas & notes

verbose = TRUE (default) prints helpful notes (what was added/dropped, derived formulas). Set to FALSE for a quieter console.

DAGassist(dag = dag_model, formula = Y ~ X + Z + C, data = df, verbose = FALSE)

Parameter Reference Table

Parameter Type Default What it does
dag dagitty object — The DAG to validate and evaluate.
formula formula or single call — Either Y ~ X + ... or a single engine call like feols(...).
data data.frame — Required unless supplied in engine call.
engine function stats::lm Modeling function (ignored if formula is a call).
engine_args named list list() Extra args for engine(...); merged with call args (call wins).
verbose logical TRUE Print formulas & notes in console.
type string "console" One of "console", "latex", "docx"/"word", "xlsx"/"excel", "text"/"txt".
out path — Output path for non-console types.
imply logical FALSE Scope: pruned-to-formula vs full-DAG evaluation.
labels named chr / data.frame NULL Rename coefficients (modelsummary coef_rename logic).
omit_intercept logical TRUE Hide intercept in printed comparison.
omit_factors logical TRUE Hide factor levels in printed comparison.
show string "all" "all", "roles", or "models".
eval_all logical FALSE Keep non-DAG RHS terms (FEs, splines, interactions) in derived models.

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They may not be fully stable and should be used with caution. We make no claims about them.
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