Package 'DAGassist'

flag bad controls (mediator/collider/desc of Y) among a candidate set

Description

flag bad controls (mediator/collider/desc of Y) among a candidate set

Usage

bad_controls_in(dag, controls, exposure, outcome)

Arguments

dag	A dagitty DAG object.
controls	Character vector of variable names.
exposure	Character; exposure node name (X).
outcome	Character; outcome node name (Y).

Value

A character vector (possibly empty) containing the elements of controls that are identified as "bad controls".

This is essentially the inverse of pick_minimal_controls(), as it returns bad controls, rather than the minimal/canonical set of good controls

Examples

d <- ggdag::dagify(
Y ~ X + M + Z,
M ~ X + Z,
C ~ X + Y,
exposure = "X",
outcome = "Y")
# M: mediator / Z: confounder / C: collider

# hypothetical candidate controls
controls <- c("Z", "M", "C")

# Flag controls that would bias the total effect of X on Y:
bad_controls_in(d, controls = c("Z","M","C"), exposure = "X", outcome = "Y")

# expected: c("M", "C")  # mediator & collider are "bad controls"; Z is OK

---

Classify DAG nodes

Description

Labels each node by causal role in a console tabular grid. This function is mostly used as an internal helper, but can be used on its own. Users are encouraged to alternatively use DAGassist::DAGassist(show=roles) for role table specific output.

Usage

classify_nodes(dag, exposure, outcome)

Arguments

dag	A dagitty DAG object.
exposure	Optional– inferred from DAG if not set; character; exposure node name (Exp.).
outcome	Optional– inferred from DAG if not set; character; outcome node name (Out.).

Value

A data.frame with one row per node and columns:

• variable (node name)

• logical flags: is_exposure, is_outcome, is_confounder, is_mediator, is_collider, is_neutral_on_treatment, is_neutral_on_outcome, is_descendant_of_mediator, is_descendant_of_collider, is_descendant_of_confounder_on_bdp, is_descendant_of_confounder_off_bdp

• role (a single primary label)

Note

Roles legend: Exp. = exposure Out. = outcome CON = confounder MED = mediator COL = collider dOut = descendant of Out. dMed = descendant of any mediator, dCol = descendant of any 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

Examples

d1 <- dagitty::dagitty("dag {X[exposure];Y[outcome] Z -> X; Z -> Y; X -> Y }") 
  classify_nodes(d1)

---

Generate and/or export report that classifies nodes, compares models, and (optionally) target causal estimands.

Description

DAGassist() validates a DAG + model specification, classifies node roles, builds minimal and canonical adjustment sets, fits comparable models, and renders a compact report in several formats (console, LaTeX fragment, DOCX, XLSX, plain text). It can also target sample-average estimands via weighting (e.g., SATE/SATT) and recover sample average controlled direct effects via sequential g-estimation (e.g., SACDE).

Usage

DAGassist(
  dag,
  formula = NULL,
  data = NULL,
  exposure,
  outcome,
  engine = stats::lm,
  labels = NULL,
  verbose = TRUE,
  type = c("console", "latex", "word", "docx", "excel", "xlsx", "text", "txt", "dwplot",
    "dotwhisker"),
  show = c("all", "roles", "models"),
  out = NULL,
  imply = FALSE,
  eval_all = FALSE,
  exclude = NULL,
  omit_intercept = TRUE,
  omit_factors = TRUE,
  bivariate = FALSE,
  estimand = c("raw", "none", "SATE", "SATT", "SACDE", "SCDE"),
  engine_args = list(),
  weights_args = list(),
  wts_omit = NULL,
  auto_acde = TRUE,
  acde = list(),
  directeffects_args = list()
)

Arguments

dag	A dagitty object (see dagitty::dagitty()).
formula	Either (a) a standard model formula Y ~ X + ..., or (b) a single engine call such as feols(Y ~ X + Z | fe, data = df, ...). When an engine call is provided, engine, data, and extra arguments are automatically extracted from the call.
data	A data.frame (or compatible, e.g. tibble). Optional if supplied via the engine call in formula.
exposure	Optional character scalar; if missing/empty, inferred from the DAG (must be unique).
outcome	Optional character scalar; if missing/empty, inferred from the DAG (must be unique).
engine	Modeling function, default stats::lm. Ignored if formula is a single engine call (in that case the function is taken from the call).
labels	list; optional variable labels (named character vector or data.frame).
verbose	logical (default TRUE). Controls verbosity in the console printer (formulas + notes).
type	output type. One of "console" (default), "latex"/"docx"/"word", "excel"/"xlsx", "text"/"txt", or the plotting types "dwplot"/"dotwhisker". For type = "latex", if no ⁠out=⁠ is supplied, a LaTeX fragment is printed to the console instead of being written to disk.
show	character vector or list; specify which sections to include in the output. One of "all" (default), "roles" (roles grid only), or "models" (model comparison only.
out	output file path for the non-console types: type="latex": a LaTeX fragment written to out (usually .tex); when omitted, the fragment is printed to the console. type="text"/"txt": a plain-text file written to out; when omitted, the report is printed to console. type="dotwhisker"/"dwplot": a image (.png) file written to out; when omitted, the plot is rendered within RStudio. type="docx"/"word": a Word (.docx) file written to out. type="excel"/"xlsx": an Excel (.xlsx) file written to out. Ignored for type="console".
imply	logical; default FALSE. Controls whether roles/sets are computed on a pruned DAG or the full DAG. If FALSE (default): restrict DAG evaluation to exposure, outcome, and terms named in the model (prune the DAG to what appears in the specification). If TRUE: evaluate on the full DAG and allow DAG-implied controls in the minimal/canonical sets; the roles table includes all DAG nodes.
eval_all	logical; default FALSE. When TRUE, retain original RHS terms that are not DAG nodes (e.g., fixed effects, interactions, splines) in derived minimal/canonical formulas. When FALSE, non-DAG RHS terms are dropped from derived formulas.
exclude	character vector or list; remove neutral controls from the canonical set. Recognized values are "nct" (drop neutral-on-treatment controls) and "nco" (drop neutral-on-outcome controls). Users can supply one or both, e.g. exclude = c("nco", "nct"); each requested variant is fitted and shown as a separate "Canon. (-...)" column in the console/model exports.
omit_intercept	logical; drop intercept rows from the model comparison display (default TRUE).
omit_factors	logical; drop factor-level rows from the model comparison display (default TRUE). This parameter only suppresses factor output; factor terms still enter the regression.
bivariate	logical; if TRUE, include a bivariate (exposure-only) specification in the comparison table in addition to the user's original and DAG-derived models (default FALSE).
estimand	character vector; causal estimand(s) for reported columns. Any of: "raw" (default), "SATE", "SATT", "SACDE" (alias "SCDE"), or "none". "raw": naive regression fits implied by the supplied engine/formulas. "SATE"/"SATT": inverse-probability weighted versions of each comparison model (via WeightIt) to target sample ATE/ATT. "SACDE"/"SCDE": for DAGs with mediator(s), adds sequential g-estimation columns: (i) unweighted sequential-g and (ii) IPW-weighted sequential-g (weights estimated without conditioning on mediators) to target the sample average controlled direct effect.
engine_args	Named list of extra arguments forwarded to engine(...). If formula is an engine call, arguments from the call are merged with engine_args (call values take precedence).
weights_args	list; arguments forwarded to WeightIt when computing IPW weights for "SATE"/"SATT" and for the weighted SACDE refit. If trim_at is supplied, weights are winsorized at the requested quantile before refitting.
wts_omit	character vector; terms to omit from the weighting (treatment) model even when eval_all = TRUE. Useful for keeping non-DAG fixed effects in the outcome model while preventing them from entering the propensity/weight model.
auto_acde	logical; if TRUE (default), automates handling conflicts between specifications and estimand arguments. Fails gracefully with a helpful error when users specify ACDE estimand for a model without mediators.
acde	list; options for the controlled direct effect workflow (estimands "SACDE"/"SCDE"). Users can override parts of the sequential g-estimation specification with named elements: m (mediators), x (baseline covariates), z (intermediate covariates), fe (fixed-effects variables), fe_as_factor (wrap fe as factor()), and include_descendants (treat descendants of mediators as mediators).
directeffects_args	Named list of arguments forwarded to DirectEffects::sequential_g() when estimand includes "SACDE" (e.g., simulation/bootstrap controls, variance estimator options).

Details

Engine-call parsing. If formula is a call (e.g., feols(Y ~ X | fe, data=df)), DAGassist extracts the engine function, formula, data argument, and any additional engine arguments directly from that call; these are merged with engine/engine_args you pass explicitly (call arguments win).

fixest tails. For engines like fixest that use | to denote FE/IV parts, DAGassist preserves any ⁠| ...⁠ tail when constructing minimal/canonical formulas (e.g., Y ~ X + controls | fe | iv(...)).

Roles grid. The roles table displays short headers:

• Exp. (exposure),

• Out. (outcome),

• CON (confounder),

• MED (mediator),

• COL (collider),

• dOut (descendant of Y),

• dMed (descendant of any mediator),

• dCol (descendant of any 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). These extra flags are used to (i) warn about bad controls, and (ii) build filtered canonical sets such as “Canonical (–NCO)” for export.

Bad controls. For total-effect estimation, DAGassist flags as ⁠bad controls⁠ any variables that are MED, COL, dOut, dMed, or dCol. These are warned in the console and omitted from the model-comparison table. Valid confounders (pre-treatment) are eligible for minimal/canonical adjustment sets.

Output types.

• console prints roles, adjustment sets, formulas (if verbose), and a compact model comparison (using {modelsummary} if available, falling back gracefully otherwise).

• latex writes or prints a LaTeX fragment you can ⁠\\input{}⁠ into a paper — it uses tabularray long tables and will include any requested Canon. (-NCO / -NCT) variants.

• docx/word writes a Word doc (respects options(DAGassist.ref_docx=...) if set).

• excel/xlsx writes an Excel workbook with tidy tables.

• text/txt writes a plain-text report for logs/notes.

• dwplot/dotwhisker produces a dot-whisker visualization of the fitted models.

Dependencies. Core requires {dagitty}. Optional enhancements: {modelsummary} (pretty tables), {broom} (fallback tidying), {rmarkdown} + pandoc (DOCX), {writexl} (XLSX), {dotwhisker}/{ggplot2} for plotting.

Raw vs Weighted SACDE. The unweighted sequential-g estimator in DirectEffects uses linear regression in its second stage. By the Frisch–Waugh–Lovell theorem, this implies an estimand that is weighted by the conditional variance of the (residualized) exposure given controls—i.e., a regression-weighted average of unit-level effects, not a sample-average controlled direct effect. DAGassist therefore reports both the raw sequential-g result and a weighted sequential-g refit (using WeightIt IPW weights estimated without mediators) to target the sample average controlled direct effect.

Value

A DAGassist_report object (a named list) returned invisibly for file/plot outputs and printed for type = "console".

The object contains:

validation

List. Output of validate_spec(): DAG validity + exposure/outcome checks.

roles

data.frame. Raw node-role flags from classify_nodes().

roles_display

data.frame. Roles table formatted for printing/export.

labels_map

Named character vector. Variable → display label map used in tables/plots.

controls_minimal

Character vector. (Legacy) One minimal adjustment set.

controls_minimal_all

List of character vectors. All minimal adjustment sets.

controls_canonical

Character vector. Canonical adjustment set (possibly empty).

controls_canonical_excl

Named list. Filtered canonical sets created by exclude.

conditions

List. Parsed conditional statements from the DAG (if any).

formulas

List. User formula plus DAG-derived formula variants (minimal/canonical/etc.).

models

List. Fitted models for each formula variant (including minimal-list fits).

bad_in_user

Character vector. RHS terms classified as mediator/collider/etc.

unevaluated

Character vector. Terms carried through but not evaluated by the engine.

unevaluated_str

Character scalar. Pretty-printed version of unevaluated.

settings

List. Print/export settings, including coef_omit and show.

.__data

data.frame or NULL. The data used to fit models (stored for downstream helpers).

For file outputs (type = "latex", "docx", "xlsx", "txt", "dotwhisker"), the returned object includes attribute file, the normalized output path.

See Also

print.DAGassist_report() and vignette("DAGassist", package = "DAGassist").

Examples

if (requireNamespace("dagitty", quietly = TRUE)) {
  g <- dagitty::dagitty("dag { Z -> X; X -> M; X -> Y; M -> Y; Z -> Y }")
  dagitty::exposures(g) <- "X"; dagitty::outcomes(g) <- "Y"
  n <- 300
  Z <- rnorm(n); X <- 0.8*Z + rnorm(n)
  M <- 0.9*X + rnorm(n)
  Y <- 0.7*X + 0.6*M + 0.3*Z + rnorm(n)
  df <- data.frame(Z, X, M, Y)

  # 1) Core: DAG-derived specs + engine-call parsing
  r <- DAGassist(g, lm(Y ~ X + Z + M, data = df))

  # 2) Target sample-average estimands via weighting (requires WeightIt)
  if (requireNamespace("WeightIt", quietly = TRUE)) {
    r2 <- DAGassist(g, lm(Y ~ X + Z + M, data = df), estimand = "SATE")
  }

  # 3) Mediator case: sequential g-estimation (requires DirectEffects)
  if (requireNamespace("DirectEffects", quietly = TRUE)) {
    r3 <- DAGassist(g, lm(Y ~ X + Z + M, data = df), estimand = "SACDE")
  }

  # 4) File export (LaTeX fragment)
  
    out <- file.path(tempdir(), "dagassist_report.tex")
    DAGassist(g, lm(Y ~ X + Z + M, data = df), type = "latex", out = out)
  
}

---

Print method for DAGassist reports

Description

Nicely prints the roles table, highlights potential bad controls, shows minimal/canonical adjustment sets, optionally shows formulas, and renders a compact model comparison (using {modelsummary} if available, falling back to {broom} or basic coef() preview).

Usage

## S3 method for class 'DAGassist_report'
print(x, ...)

Arguments

x	A "DAGassist_report" object returned by DAGassist().
...	Additional arguments (currently unused; present for S3 compatibility).

Details

The printer respects the verbose flag in the report: when TRUE, it includes formulas and a brief note on variables added by DAG logic (minimal and canonical sets). Fitting errors are shown inline per model column and do not abort printing.

Value

Invisibly returns x.

---

Print node classifications (aligned)

Description

Print node classifications (aligned)

Usage

## S3 method for class 'DAGassist_roles'
print(x, n = Inf, ...)

Arguments

x	Output of classify_nodes() (class "DAGassist_roles")
n	Max rows to print (default all)
...	(ignored)

Value

Invisibly returns x

---

Minimal, clean printout for validation results with color coding

Description

Minimal, clean printout for validation results with color coding

Usage

## S3 method for class 'DAGassist_validation'
print(x, n = 10, ...)

Arguments

x	the list (class out) from validate_spec
n	Max number of issues to show (default 10).
...	Ignored.

Value

Invisibly returns x.

---