Get started with DAGassist

DAGassist checks your regression against your causal diagram. It infers which controls identify the estimand of interst, re-fits the model with the controls the diagram implies, and reports the comparison. This guide walks through the workflow in five steps:

Declare the effect you want to estimate.
Draw a DAG of how you think the data were generated.
Classify each control by its causal role.
Re-estimate the model with DAG-derived adjustment sets.
Target your estimand explicitly.

The running example asks whether higher income increases voter turnout. The data, turnout_data, are simulated, so the right answers are known.

library(DAGassist)

Step 1: Declare your estimand

Before choosing controls, decide which effect you want (Lundberg et al. 2021). Income could affect turnout in two ways: directly, and by raising political interest, which in turn raises turnout. That gives two different questions:

The total effect. How much does turnout change when income rises, through every path? In turnout_data, the answer is 0.50.
The direct effect. How much does turnout change when income rises but political interest is held fixed? The answer is 0.30.

The choice matters because the same control can be required for one question and inadmissable for the other. This guide targets the total effect.

Step 2: Draw your DAG

A directed acyclic graph (DAG) encodes your assumptions about how the data were generated. Each edge (arrow) is a direct causal effect; each missing edge is an assumption that there is none. For introductions to DAGs, see Pearl (2009), Elwert (2013), and Hünermund et al. (2025).

The easiest way to write a DAG in R is ggdag::dagify(), which uses formula syntax: each line lists a variable and its direct causes. Note that DAGassist requires that node names must match the column names in your data.

dag <- ggdag::dagify(
  turnout  ~ income + age + state + polint + elect_comp,
  income   ~ age + state + industry,
  polint   ~ income,
  industry ~ age,
  exposure = "income",
  outcome  = "turnout"
)

Alternatively, you can also draw the DAG in the DAGitty web tool and paste its code into dagitty::dagitty().

The turnout DAG. Age and state affect income and turnout; age affects industry, which affects income; income affects political interest, which affects turnout; election competitiveness affects turnout.

In our example, age and state affect both income and turnout. Political interest carries part of income’s effect to turnout. Industry affects income only, and election competitiveness affects turnout only.

Step 3: Classify your controls

Now take the regression you would have run. A common instinct is to control for everything available. Pass the DAG and that regression to DAGassist(). The regression is written as the full model call, so DAGassist() can re-fit it with other controls.

DAGassist(dag,
          lm(turnout ~ income + state + age + polint + industry + elect_comp,
             data = turnout_data),
          show = "roles")
#> DAGassist Report: 
#> 
#> Roles:
#> variable    role        Exp.  Out.  conf  med  col  dOut  dMed  dCol  dConfOn  dConfOff  NCT  NCO
#> income      exposure    x                                                                        
#> turnout     outcome           x                                                                  
#> age         confounder              x                                                            
#> state       confounder              x                                                            
#> polint      mediator                      x                                                      
#> elect_comp  nco                                                                               x  
#> industry    nct                                                       x                  x       
#> 
#>  (!) Bad controls in your formula: {polint}
#> 
#> Roles legend: Exp. = exposure/treatment; 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

Classifying roles needs only the DAG and the formula, not the data. For the total effect:

age and state are confounders. They must be controlled for.
polint is a mediator and a bad control. Controlling for it removes the part of income’s effect that runs through political interest.
industry affects only income, and elect_comp affects only turnout. Both are neutral controls: safe to include, but not required.

Causal roles and adjustment sets defines every role DAGassist can assign.

Step 4: Re-estimate with DAG-derived adjustment sets

With show = "models", DAGassist() also re-fits the model with two adjustment sets derived from the DAG. The minimal set contains only the controls needed to block every back-door path. The canonical set also adds every safe control.

DAGassist(dag,
          lm(turnout ~ income + state + age + polint + industry + elect_comp,
             data = turnout_data),
          show = "models", type = "text", verbose = FALSE)

Term	Original	Minimal 1	Canonical
income	0.281***	0.493***	0.492***
	(0.016)	(0.016)	(0.015)
state	0.331***	0.324***	0.332***
	(0.017)	(0.019)	(0.018)
age	0.275***	0.273***	0.267***
	(0.017)	(0.020)	(0.019)
polint	0.420***
	(0.014)
industry	-0.017		-0.010
	(0.015)		(0.016)
elect_comp	0.500***		0.506***
	(0.014)		(0.015)
Num.Obs.	5000	5000	5000
R2	0.596	0.423	0.525

p-value legend: + < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Controls (minimal): {age, state}.
Controls (canonical): {age, elect_comp, industry, state}.

Both DAG-derived models estimate income’s effect at 0.49, close to the true total effect of 0.50. The original regression returns 0.28; by controlling for the mediator, the original model estimates something close to the direct effect (0.30). In the console, the full report also includes balance diagnostics, which flag when missing data leave the models with different samples.

To see the comparison as a plot, set type = "dotwhisker":

DAGassist(dag,
          lm(turnout ~ income + state + age + polint + industry + elect_comp,
             data = turnout_data),
          type = "dotwhisker")

Dot-and-whisker plot of the income coefficient: about 0.28 in the original model and about 0.49 in the minimal and canonical models.

Step 5: Target your estimand explicitly

The DAG-derived regressions recover the total effect here because the model is linear and has no interactions. The estimand argument targets an effect directly, whatever the model:

estimand = "total" adds inverse-probability-weighted estimates of the average total effect.
estimand = "direct" adds sequential g-estimates of the average controlled direct effect.

DAGassist(dag,
          lm(turnout ~ income + state + age + polint + industry + elect_comp,
             data = turnout_data),
          estimand = c("total", "direct"),
          show = "models", type = "text", verbose = FALSE)

Term	Original	Total Minimal 1 (Raw)	Total Canonical (Raw)	Total Minimal 1 (Weighted)	Total Canonical (Weighted)	Direct (Raw)	Direct (Weighted)
income	0.281***	0.493***	0.492***	0.495***	0.493***	0.281***	0.280***
	(0.016)	(0.016)	(0.015)	(0.025)	(0.029)	(0.014)	(0.027)
state	0.331***	0.324***	0.332***			0.331***	0.343***
	(0.017)	(0.019)	(0.018)			(0.017)	(0.031)
age	0.275***	0.273***	0.267***			0.275***	0.272***
	(0.017)	(0.020)	(0.019)			(0.017)	(0.028)
polint	0.420***
	(0.014)
industry	-0.017		-0.010			-0.017	-0.014
	(0.015)		(0.016)			(0.015)	(0.024)
elect_comp	0.500***		0.506***			0.500***	0.476***
	(0.014)		(0.015)			(0.014)	(0.025)
Num.Obs.	5000	5000	5000	5000	5000	5000	5000
R2	0.596	0.423	0.525	0.339	0.441

p-value legend: + < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Controls (minimal): {age, state}.
Controls (canonical): {age, elect_comp, industry, state}.

The weighted total-effect estimates (0.49–0.50) agree with the regression estimates, and the direct-effect estimates (0.28) are close to the true direct effect of 0.30. Each number now corresponds to an explict estimand. Total and direct effects explains how each column is estimated, how to read the weight diagnostics, and what each estimand assumes.

Next steps

Every conclusion above assumes the DAG is correct. Thus, it is important that you use DAGassist’s uncertainty tests to see if model results are sensitive to uncertainty in the DAG. For example, what if political interest raises income, rather than the reverse?

pdag_robustness(dag,
                formula = turnout ~ income + state + age + polint + industry + elect_comp,
                uncertain_edges = "income -- polint")
#> 
#> PDAG robustness summary:
#> - uncertain edges specified: 1
#> - worlds evaluated (acyclic orientations): 2
#> - minimal adjustment set changed: yes
#> - canonical adjustment set changed: yes
#> - covariate role changed: mediator -> ambiguous (confounder / mediator) for polint (good/bad control flip)
#> - re-estimation recommended: yes

If that edge is reversed, political interest becomes a confounder, changing the adjustment set and estimates. Robustness to DAG uncertainty explains uncertainty testing in greater detail.

Further documentation and tutorials:

Causal roles and adjustment sets covers every role and both adjustment sets.
Total and direct effects covers estimand recovery and its assumptions.
Robustness to DAG uncertainty tests uncertain and missing arrows.
Exporting reports writes the report to LaTeX, Word, Excel, plain text, or a plot.
Supported model engines lists the estimators DAGassist works with beyond lm().

References

Elwert, Felix. 2013. “Graphical Causal Models.” In Handbook of Causal Analysis for Social Research, edited by Stephen L. Morgan, vol. 54. Springer. https://doi.org/10.1007/978-1-4471-6699-3_13.

Hünermund, Paul, Beyers Louw, and Mikko Rönkkö. 2025. “The Choice of Control Variables in Empirical Management Research: How Causal Diagrams Can Inform the Decision.” Leadership Quarterly 36: 1–15.

Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86: 532–65. https://doi.org/10.1177/00031224211004187.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. Cambridge University Press.