3 DAGs, ADMGs, Identification, and Estimation

The potential-outcomes chapter asked when an observed data set can tell us about $E[Y(1)-Y(0)]$. Graphs give one way to state the assumptions behind the answer. A graph records which variables are allowed to cause other variables, and whether some observed variables may share unobserved common causes. Once the graph is stated, identification is a graph problem. Estimation comes after that: estimate the functional that the graph identified.

I use dagitty for graphs and backdoor adjustment, causaleffect for the general Pearl-Shpitser ID algorithm, and small direct estimators for the examples. The goal is to keep the order clear: graph first, identified functional second, estimator third.

3.1 DAGs and ADMGs

A directed acyclic graph, or DAG, has directed arrows such as X -> A. In causal work, an arrow means a direct causal relation is allowed by the model. Absence of an arrow is also a substantive assumption.

An acyclic directed mixed graph, or ADMG, adds bidirected arrows such as A <-> Y. A bidirected edge represents an unobserved common cause. This is useful because many econometric applications have variables we cannot measure: ability, preferences, latent health, firm quality, and so on.

The graph is not an estimator. It is a compact statement of assumptions. The workflow is:

write down a DAG or ADMG;
ask whether the target effect is identified;
estimate the identified functional from data.

3.2 A DAG: Backdoor Adjustment

Start with a simple observational study. A is treatment, Y is the outcome, and X is an observed confounder:

g_dag <- dagitty("dag {
  X -> A
  X -> Y
  A -> Y
}")
coordinates(g_dag) <- list(
  x = c(X = 1, A = 0, Y = 2),
  y = c(X = 1, A = 0, Y = 0)
)

ggdag(g_dag) + theme_dag_blank()

Figure 3.1: Backdoor DAG: X is an observed confounder of A → Y.

The graph says X affects both treatment and outcome. It also says there is no unobserved common cause between A and Y after we condition on X. In this case the treatment effect is identifiable by backdoor adjustment.

adj_sets <- adjustmentSets(g_dag, exposure = "A", outcome = "Y")
adj_sets

{ X }

The adjustment set is $\{X\}$. Under this graph,

\[ E[Y(a)] = E_X\{E(Y \mid A=a, X)\}. \]

So the graph has translated a causal query into an observed-data functional.

3.3 Estimating the DAG Effect with AIPW

Here is a small simulated data set where X confounds the treatment–outcome association.

set.seed(2026)

logistic <- function(x) 1 / (1 + exp(-x))

n  <- 400
X  <- rnorm(n)
pA <- logistic(-0.2 + 0.8 * X)
A  <- as.numeric(runif(n) < pA)
Y  <- 1 + 1.5 * A + 0.7 * X + 0.3 * rnorm(n)

df_dag <- data.frame(X = X, A = A, Y = Y)
head(df_dag)

            X A          Y
1  0.52058907 0  1.6530538
2 -1.07969076 0  0.1907760
3  0.13923812 0  1.0984536
4 -0.08474878 0  1.0941604
5 -0.66663962 1  1.8965341
6 -2.51608903 0 -0.8245336

Because the graph is backdoor-identified, use AIPW with the adjustment set the graph chose. The function below implements the AIPW point estimate and an EIF-based standard error.

aipw_backdoor <- function(data, treatment, outcome, adjust) {
  fmla_y <- as.formula(paste0(outcome, " ~ ", treatment, " * (",
                              paste(adjust, collapse = " + "), ")"))
  fmla_a <- as.formula(paste0(treatment, " ~ ",
                              paste(adjust, collapse = " + ")))

  mu_fit <- lm(fmla_y, data = data)
  pi_fit <- glm(fmla_a, data = data, family = binomial())

  d1 <- data; d1[[treatment]] <- 1
  d0 <- data; d0[[treatment]] <- 0
  mu1 <- predict(mu_fit, newdata = d1)
  mu0 <- predict(mu_fit, newdata = d0)
  pi1 <- predict(pi_fit, newdata = data, type = "response")

  w  <- data[[treatment]]; y <- data[[outcome]]
  if1 <- w     * (y - mu1) / pi1       + mu1
  if0 <- (1-w) * (y - mu0) / (1 - pi1) + mu0
  psi  <- if1 - if0

  est  <- mean(psi)
  se   <- sd(psi) / sqrt(length(psi))
  ci   <- est + c(-1, 1) * qnorm(0.975) * se
  data.frame(estimand = "Backdoor ACE",
             estimate = round(est, 4),
             lower_95 = round(ci[1], 4),
             upper_95 = round(ci[2], 4),
             se       = round(se, 4))
}

aipw_backdoor(df_dag, "A", "Y", adjust = "X") |> kable()

estimand	estimate	lower_95	upper_95	se
Backdoor ACE	1.4886	1.427	1.5502	0.0314

The graph chose the adjustment set; the estimator used that set. The point estimate recovers the true ACE of 1.5 within sampling noise.

The standard error here, sd(psi) / sqrt(n), is the influence-function standard error of the AIPW estimator. It is asymptotically valid when the nuisance functions ($\mu$ and $\pi$) are regular parametric fits, as they are here. With flexible machine-learning nuisances it is not valid as written: you then need cross-fitting (sample splitting) so the nuisance estimates are independent of the fold being evaluated, and the variance should still be computed from the efficient influence function (or by bootstrap). Treating a full-sample ML AIPW estimate with this plug-in SE will generally understate uncertainty.

3.4 An ADMG: Front-Door Identification

Now suppose treatment and outcome share an unobserved common cause. A simple adjustment argument no longer works. The front-door ADMG has a mediator M that transmits the effect of A to Y, while A and Y are hidden-confounded:

g_fd <- dagitty("dag {
  A -> M
  M -> Y
  A <-> Y
}")
coordinates(g_fd) <- list(
  x = c(A = 0, M = 1, Y = 2),
  y = c(A = 0, M = 0, Y = 0)
)

ggdag(g_fd) + theme_dag_blank()

Figure 3.2: Front-door ADMG: A and Y share an unobserved common cause (curved bidirected edge), but the effect propagates through the mediator M.

The bidirected edge A <-> Y means that ordinary backdoor adjustment is not available. Indeed dagitty reports no adjustment set:

adjustmentSets(g_fd, exposure = "A", outcome = "Y")

But the effect is still identified by the front-door criterion (Pearl, 1995). I call the Pearl-Shpitser ID algorithm in causaleffect to obtain the symbolic expression. The function wants the graph as an igraph object with bidirected edges encoded as two directed edges marked description = "U":

g_fd_ig <- graph_from_literal(A -+ M, M -+ Y, A -+ Y, Y -+ A)
# Mark the A<->Y bidirected edge (encoded as the two directed edges A->Y and
# Y->A) by endpoint name rather than by hardcoded edge index -- igraph's
# internal edge ordering after graph_from_literal() is not part of its
# documented contract, so indices like c(2, 4) can silently point at the
# wrong edges if igraph's internals, the vertex/edge creation order, or the
# package version ever changes.
edge_ends <- ends(g_fd_ig, E(g_fd_ig))
confounded <- (edge_ends[, 1] == "A" & edge_ends[, 2] == "Y") |
              (edge_ends[, 1] == "Y" & edge_ends[, 2] == "A")
E(g_fd_ig)$description[confounded] <- "U"
expr_fd <- causal.effect(y = "Y", x = "A", G = g_fd_ig, simp = TRUE)
cat(expr_fd, "\n")

\sum_{M}P(M|A)\left(\sum_{A}P(Y|A,M)P(A)\right)

The expression $\sum_M P(M\mid A)\sum_{A'} P(Y\mid A', M) P(A')$ is precisely the front-door formula.

3.5 Estimating the ADMG Effect with the Front-Door Formula

Simulate a front-door setting. The latent U is not included in the observed data; it is represented in the graph by A <-> Y.

set.seed(2027)

n     <- 400
U     <- rnorm(n)
A_fd  <- as.numeric(runif(n) < logistic(-0.1 + 0.8 * U))
M     <- as.numeric(runif(n) < logistic(-0.4 + 1.2 * A_fd))
Y_fd  <- as.numeric(runif(n) < logistic(-1.0 + 0.9 * M + 0.7 * U))

df_fd <- data.frame(A = A_fd, M = M, Y = Y_fd)
head(df_fd)

The front-door point estimator is a direct evaluation of the identification expression:

\[ E[Y(a)] = \sum_m P(M=m\mid A=a)\sum_{a'} P(Y=1\mid A=a', M=m) P(A=a'). \]

For binary $A$, $M$, $Y$ we plug in sample proportions:

front_door_ace <- function(data) {
  pA <- mean(data$A)
  p_marginal_A <- c("0" = 1 - pA, "1" = pA)

  # P(M | A): rows indexed by A, columns by M
  pM_given_A <- prop.table(table(data$A, data$M), margin = 1)
  # P(Y=1 | A, M)
  pY_given_AM <- with(data, tapply(Y, list(A, M), mean))

  # E[Y(a)] for a in {0, 1}
  ey_do <- function(a) {
    m_levels <- as.numeric(colnames(pM_given_A))
    sum(sapply(m_levels, function(m) {
      mar <- pM_given_A[as.character(a), as.character(m)]
      inner <- sum(sapply(c(0, 1), function(ap)
        pY_given_AM[as.character(ap), as.character(m)] *
          p_marginal_A[as.character(ap)]))
      mar * inner
    }))
  }
  ey_do(1) - ey_do(0)
}

# Bootstrap for SE
set.seed(99)
B   <- 500
boot_ace <- replicate(B, {
  idx <- sample.int(nrow(df_fd), replace = TRUE)
  front_door_ace(df_fd[idx, ])
})

est_fd <- front_door_ace(df_fd)
se_fd  <- sd(boot_ace)
ci_fd  <- est_fd + c(-1, 1) * qnorm(0.975) * se_fd

data.frame(estimand = "Front-door ACE",
           estimate = round(est_fd, 4),
           lower_95 = round(ci_fd[1], 4),
           upper_95 = round(ci_fd[2], 4),
           se       = round(se_fd, 4)) |> kable()

estimand	estimate	lower_95	upper_95	se
Front-door ACE	0.0775	0.0403	0.1146	0.0189

This is the practical advantage of separating identification from estimation. State the graph first. Then use the estimator that matches the identified functional.

3.6 General ID Algorithm

The named routes above are useful because they are easy to estimate: backdoor adjustment and the front-door formula. ADMGs can be more general. The Pearl-Shpitser ID algorithm asks the broader question:

Can P(Y | do(A)) be written using only the observed joint law P(V)?

The algorithm works recursively:

remove variables that are not ancestors of the outcome;
add interventions that are irrelevant after graph surgery;
split the remaining graph into bidirected districts;
identify each district as a factor of the observed law, if possible;
return a hedge witness when no such reduction is possible.

For the front-door graph, the general ID algorithm succeeds and returns the same front-door formula we obtained above. For more complex ADMGs it may yield novel functionals that no named estimator covers — this is where the plug-in approach is essential.

3.7 When the Graph Says No

The bow graph has both a direct causal edge and unobserved confounding between the same two variables:

g_bow <- dagitty("dag {
  A -> Y
  A <-> Y
}")
coordinates(g_bow) <- list(
  x = c(A = 0, Y = 1),
  y = c(A = 0, Y = 0)
)

ggdag(g_bow) + theme_dag_blank()

Figure 3.3: Bow ADMG: a direct edge A → Y plus a bidirected A ↔︎ Y. The effect is not identified.

The effect of A on Y is not identified in this ADMG. The observed association mixes the direct causal effect with the latent common cause.

g_bow_ig <- graph_from_literal(A -+ Y, Y -+ A)
g_bow_ig <- set_edge_attr(g_bow_ig, "description",
                          index = c(1, 2), value = "U")
g_bow_ig <- add_edges(g_bow_ig, c("A", "Y"))   # add the direct A -> Y

result <- tryCatch(
  causal.effect(y = "Y", x = "A", G = g_bow_ig, simp = TRUE),
  error = function(e) paste("Not identifiable:", conditionMessage(e))
)
result

[1] "Not identifiable: Not identifiable."

causaleffect raises an error message indicating the effect is not identifiable. This is a useful failure mode. If the graph does not justify the causal effect, the estimator should not manufacture one.

3.8 Practical Workflow

For applied work, a useful workflow is:

write the graph before looking at estimates;
use bidirected edges for latent common causes;
ask dagitty::adjustmentSets(graph, exposure, outcome) — if non-empty, you have a backdoor route;
if no adjustment set exists, call causaleffect::causal.effect(y, x, G) — if it returns an expression, the effect is identified by a more general route; if it errors, the effect is not identified in this ADMG;
estimate using AIPW for backdoor effects, the front-door formula when the algorithm returns that pattern, or a plug-in evaluation of the symbolic expression for general ID functionals;
report the graph and the identification route along with the estimate.

# DAGs, ADMGs, Identification, and Estimation ```{r} #| include: false suppressPackageStartupMessages({ library(dagitty) library(ggdag) library(ggplot2) library(igraph) library(causaleffect) library(dplyr) library(knitr) }) ``` The potential-outcomes chapter asked when an observed data set can tell us about $E[Y(1)-Y(0)]$. Graphs give one way to state the assumptions behind the answer. A graph records which variables are allowed to cause other variables, and whether some observed variables may share unobserved common causes. Once the graph is stated, identification is a graph problem. Estimation comes after that: estimate the functional that the graph identified. I use `dagitty` for graphs and backdoor adjustment, `causaleffect` for the general Pearl-Shpitser ID algorithm, and small direct estimators for the examples. The goal is to keep the order clear: graph first, identified functional second, estimator third. ## DAGs and ADMGs A directed acyclic graph, or DAG, has directed arrows such as `X -> A`. In causal work, an arrow means a direct causal relation is allowed by the model. Absence of an arrow is also a substantive assumption. An acyclic directed mixed graph, or ADMG, adds bidirected arrows such as `A <-> Y`. A bidirected edge represents an unobserved common cause. This is useful because many econometric applications have variables we cannot measure: ability, preferences, latent health, firm quality, and so on. The graph is not an estimator. It is a compact statement of assumptions. The workflow is: 1. write down a DAG or ADMG; 2. ask whether the target effect is identified; 3. estimate the identified functional from data. ## A DAG: Backdoor Adjustment Start with a simple observational study. `A` is treatment, `Y` is the outcome, and `X` is an observed confounder: ```{r} g_dag <- dagitty("dag { X -> A X -> Y A -> Y }") coordinates(g_dag) <- list( x = c(X = 1, A = 0, Y = 2), y = c(X = 1, A = 0, Y = 0) ) ``` ```{r} #| label: fig-dag-backdoor #| fig-cap: "Backdoor DAG: X is an observed confounder of A → Y." #| fig-width: 5 #| fig-height: 3 ggdag(g_dag) + theme_dag_blank() ``` The graph says `X` affects both treatment and outcome. It also says there is no unobserved common cause between `A` and `Y` after we condition on `X`. In this case the treatment effect is identifiable by **backdoor adjustment**. ```{r} adj_sets <- adjustmentSets(g_dag, exposure = "A", outcome = "Y") adj_sets ``` The adjustment set is $\{X\}$. Under this graph, $$ E[Y(a)] = E_X\{E(Y \mid A=a, X)\}. $$ So the graph has translated a causal query into an observed-data functional. ## Estimating the DAG Effect with AIPW Here is a small simulated data set where `X` confounds the treatment–outcome association. ```{r} #| warning: false #| message: false #| cache: true set.seed(2026) logistic <- function(x) 1 / (1 + exp(-x)) n <- 400 X <- rnorm(n) pA <- logistic(-0.2 + 0.8 * X) A <- as.numeric(runif(n) < pA) Y <- 1 + 1.5 * A + 0.7 * X + 0.3 * rnorm(n) df_dag <- data.frame(X = X, A = A, Y = Y) head(df_dag) ``` Because the graph is backdoor-identified, use AIPW with the adjustment set the graph chose. The function below implements the AIPW point estimate and an EIF-based standard error. ```{r} aipw_backdoor <- function(data, treatment, outcome, adjust) { fmla_y <- as.formula(paste0(outcome, " ~ ", treatment, " * (", paste(adjust, collapse = " + "), ")")) fmla_a <- as.formula(paste0(treatment, " ~ ", paste(adjust, collapse = " + "))) mu_fit <- lm(fmla_y, data = data) pi_fit <- glm(fmla_a, data = data, family = binomial()) d1 <- data; d1[[treatment]] <- 1 d0 <- data; d0[[treatment]] <- 0 mu1 <- predict(mu_fit, newdata = d1) mu0 <- predict(mu_fit, newdata = d0) pi1 <- predict(pi_fit, newdata = data, type = "response") w <- data[[treatment]]; y <- data[[outcome]] if1 <- w * (y - mu1) / pi1 + mu1 if0 <- (1-w) * (y - mu0) / (1 - pi1) + mu0 psi <- if1 - if0 est <- mean(psi) se <- sd(psi) / sqrt(length(psi)) ci <- est + c(-1, 1) * qnorm(0.975) * se data.frame(estimand = "Backdoor ACE", estimate = round(est, 4), lower_95 = round(ci[1], 4), upper_95 = round(ci[2], 4), se = round(se, 4)) } aipw_backdoor(df_dag, "A", "Y", adjust = "X") |> kable() ``` The graph chose the adjustment set; the estimator used that set. The point estimate recovers the true ACE of 1.5 within sampling noise. ::: {.callout-note} The standard error here, `sd(psi) / sqrt(n)`, is the influence-function standard error of the AIPW estimator. It is asymptotically valid when the nuisance functions ($\mu$ and $\pi$) are regular parametric fits, as they are here. With flexible machine-learning nuisances it is **not** valid as written: you then need cross-fitting (sample splitting) so the nuisance estimates are independent of the fold being evaluated, and the variance should still be computed from the efficient influence function (or by bootstrap). Treating a full-sample ML AIPW estimate with this plug-in SE will generally understate uncertainty. ::: ## An ADMG: Front-Door Identification Now suppose treatment and outcome share an unobserved common cause. A simple adjustment argument no longer works. The front-door ADMG has a mediator `M` that transmits the effect of `A` to `Y`, while `A` and `Y` are hidden-confounded: ```{r} g_fd <- dagitty("dag { A -> M M -> Y A <-> Y }") coordinates(g_fd) <- list( x = c(A = 0, M = 1, Y = 2), y = c(A = 0, M = 0, Y = 0) ) ``` ```{r} #| label: fig-front-door #| fig-cap: "Front-door ADMG: A and Y share an unobserved common cause (curved bidirected edge), but the effect propagates through the mediator M." #| fig-width: 5 #| fig-height: 3 ggdag(g_fd) + theme_dag_blank() ``` The bidirected edge `A <-> Y` means that ordinary backdoor adjustment is not available. Indeed `dagitty` reports no adjustment set: ```{r} adjustmentSets(g_fd, exposure = "A", outcome = "Y") ``` But the effect is still identified by the front-door criterion (Pearl, 1995). I call the Pearl-Shpitser ID algorithm in `causaleffect` to obtain the symbolic expression. The function wants the graph as an `igraph` object with bidirected edges encoded as two directed edges marked `description = "U"`: ```{r} g_fd_ig <- graph_from_literal(A -+ M, M -+ Y, A -+ Y, Y -+ A) # Mark the A<->Y bidirected edge (encoded as the two directed edges A->Y and # Y->A) by endpoint name rather than by hardcoded edge index -- igraph's # internal edge ordering after graph_from_literal() is not part of its # documented contract, so indices like c(2, 4) can silently point at the # wrong edges if igraph's internals, the vertex/edge creation order, or the # package version ever changes. edge_ends <- ends(g_fd_ig, E(g_fd_ig)) confounded <- (edge_ends[, 1] == "A" & edge_ends[, 2] == "Y") | (edge_ends[, 1] == "Y" & edge_ends[, 2] == "A") E(g_fd_ig)$description[confounded] <- "U" expr_fd <- causal.effect(y = "Y", x = "A", G = g_fd_ig, simp = TRUE) cat(expr_fd, "\n") ``` The expression $\sum_M P(M\mid A)\sum_{A'} P(Y\mid A', M) P(A')$ is precisely the front-door formula. ## Estimating the ADMG Effect with the Front-Door Formula Simulate a front-door setting. The latent `U` is not included in the observed data; it is represented in the graph by `A <-> Y`. ```{r} #| warning: false #| message: false #| cache: true set.seed(2027) n <- 400 U <- rnorm(n) A_fd <- as.numeric(runif(n) < logistic(-0.1 + 0.8 * U)) M <- as.numeric(runif(n) < logistic(-0.4 + 1.2 * A_fd)) Y_fd <- as.numeric(runif(n) < logistic(-1.0 + 0.9 * M + 0.7 * U)) df_fd <- data.frame(A = A_fd, M = M, Y = Y_fd) head(df_fd) ``` The front-door point estimator is a direct evaluation of the identification expression: $$ E[Y(a)] = \sum_m P(M=m\mid A=a)\sum_{a'} P(Y=1\mid A=a', M=m) P(A=a'). $$ For binary $A$, $M$, $Y$ we plug in sample proportions: ```{r} front_door_ace <- function(data) { pA <- mean(data$A) p_marginal_A <- c("0" = 1 - pA, "1" = pA) # P(M | A): rows indexed by A, columns by M pM_given_A <- prop.table(table(data$A, data$M), margin = 1) # P(Y=1 | A, M) pY_given_AM <- with(data, tapply(Y, list(A, M), mean)) # E[Y(a)] for a in {0, 1} ey_do <- function(a) { m_levels <- as.numeric(colnames(pM_given_A)) sum(sapply(m_levels, function(m) { mar <- pM_given_A[as.character(a), as.character(m)] inner <- sum(sapply(c(0, 1), function(ap) pY_given_AM[as.character(ap), as.character(m)] * p_marginal_A[as.character(ap)])) mar * inner })) } ey_do(1) - ey_do(0) } # Bootstrap for SE set.seed(99) B <- 500 boot_ace <- replicate(B, { idx <- sample.int(nrow(df_fd), replace = TRUE) front_door_ace(df_fd[idx, ]) }) est_fd <- front_door_ace(df_fd) se_fd <- sd(boot_ace) ci_fd <- est_fd + c(-1, 1) * qnorm(0.975) * se_fd data.frame(estimand = "Front-door ACE", estimate = round(est_fd, 4), lower_95 = round(ci_fd[1], 4), upper_95 = round(ci_fd[2], 4), se = round(se_fd, 4)) |> kable() ``` This is the practical advantage of separating identification from estimation. State the graph first. Then use the estimator that matches the identified functional. ## General ID Algorithm The named routes above are useful because they are easy to estimate: backdoor adjustment and the front-door formula. ADMGs can be more general. The Pearl-Shpitser ID algorithm asks the broader question: ```text Can P(Y | do(A)) be written using only the observed joint law P(V)? ``` The algorithm works recursively: 1. remove variables that are not ancestors of the outcome; 2. add interventions that are irrelevant after graph surgery; 3. split the remaining graph into bidirected districts; 4. identify each district as a factor of the observed law, if possible; 5. return a hedge witness when no such reduction is possible. For the front-door graph, the general ID algorithm succeeds and returns the same front-door formula we obtained above. For more complex ADMGs it may yield novel functionals that no named estimator covers — this is where the plug-in approach is essential. ## When the Graph Says No The **bow graph** has both a direct causal edge and unobserved confounding between the same two variables: ```{r} g_bow <- dagitty("dag { A -> Y A <-> Y }") coordinates(g_bow) <- list( x = c(A = 0, Y = 1), y = c(A = 0, Y = 0) ) ``` ```{r} #| label: fig-bow #| fig-cap: "Bow ADMG: a direct edge A → Y plus a bidirected A ↔ Y. The effect is not identified." #| fig-width: 4 #| fig-height: 2.5 ggdag(g_bow) + theme_dag_blank() ``` The effect of `A` on `Y` is not identified in this ADMG. The observed association mixes the direct causal effect with the latent common cause. ```{r} g_bow_ig <- graph_from_literal(A -+ Y, Y -+ A) g_bow_ig <- set_edge_attr(g_bow_ig, "description", index = c(1, 2), value = "U") g_bow_ig <- add_edges(g_bow_ig, c("A", "Y")) # add the direct A -> Y result <- tryCatch( causal.effect(y = "Y", x = "A", G = g_bow_ig, simp = TRUE), error = function(e) paste("Not identifiable:", conditionMessage(e)) ) result ``` `causaleffect` raises an error message indicating the effect is not identifiable. This is a useful failure mode. If the graph does not justify the causal effect, the estimator should not manufacture one. ## Practical Workflow For applied work, a useful workflow is: 1. write the graph before looking at estimates; 2. use bidirected edges for latent common causes; 3. ask `dagitty::adjustmentSets(graph, exposure, outcome)` — if non-empty, you have a backdoor route; 4. if no adjustment set exists, call `causaleffect::causal.effect(y, x, G)` — if it returns an expression, the effect is identified by a more general route; if it errors, the effect is not identified in this ADMG; 5. estimate using AIPW for backdoor effects, the front-door formula when the algorithm returns that pattern, or a plug-in evaluation of the symbolic expression for general ID functionals; 6. report the graph and the identification route along with the estimate.