Causal Diagrams in R

Draw your causal assumptions with causal directed acyclic graphs (DAGs)

The basic idea

Specify your causal question
Use domain knowledge
Write variables as nodes
Write causal pathways as arrows (edges)

ggdag

Step 1: Specify your DAG

library(ggdag)
dagify(
  cancer ~ smoking, 
  coffee ~ smoking
)library(ggdag)
dagify(
  cancer ~ smoking, 
  coffee ~ smoking
)library(ggdag)
dagify(
  cancer ~ smoking, 
  coffee ~ smoking
)

Step 1: Specify your DAG

dagify(
  cancer ~ smoking, 
  coffee ~ smoking 
) |> ggdag()

Step 1: Specify your DAG

Step 1: Specify your DAG

dagify(
  cancer ~ smoking + coffee, 
  coffee ~ smoking 
) |> ggdag()

Step 1: Specify your DAG

Your Turn 1 (`04-dags-exercises.qmd`)

Specify a DAG with `dagify()`. Write your assumption that `smoking` causes `cancer` as a formula.

We’re going to assume that coffee does not cause cancer, so there’s no formula for that. But we still need to declare our causal question. Specify “coffee” as the exposure and “cancer” as the outcome (both in quotations marks).

Plot the DAG using `ggdag()`

05:00

Your Turn 1 (`02-dags-exercises.qmd`)

coffee_cancer_dag <- dagify(
  cancer ~ smoking,
  smoking ~ addictive,
  coffee ~ addictive,
  exposure = "coffee",
  outcome = "cancer",
  labels = c(
    "coffee" = "Coffee", 
    "cancer" = "Lung Cancer", 
    "smoking" = "Smoking", 
    "addictive" = "Addictive \nBehavior"
  )
)

ggdag(coffee_cancer_dag)

Causal effects and backdoor paths

Ok, correlation != causation. But why not?

We want to know if x -> y…

But other paths also cause associations

`ggdag_paths()`

Identify “backdoor” paths

ggdag_paths(smk_wt_dag)

Your Turn 2

Call `tidy_dagitty()` on `coffee_cancer_dag` to create a tidy DAG, then pass the results to `dag_paths()`. What’s different about these data?

Plot the open paths with `ggdag_paths()`. (Just give it `coffee_cancer_dag` rather than using `dag_paths()`; the quick plot function will do that for you.) Remember, since we assume there is no causal path from coffee to lung cancer, any open paths must be confounding pathways.

05:00

Your Turn 2

coffee_cancer_dag |>
  tidy_dagitty() |>
  dag_paths()

# A DAG with 4 nodes and 3 edges
#
# Exposure: coffee
# Outcome: cancer
#
# A tibble: 5 × 11
  set   name          x      y direction to      xend   yend
  <chr> <chr>     <dbl>  <dbl> <fct>     <chr>  <dbl>  <dbl>
1 1     addictive 0.616 -1.27  ->        coff…  0.185 -0.127
2 1     addictive 0.616 -1.27  ->        smok…  1.09  -2.52 
3 1     cancer    1.52  -3.66  <NA>      <NA>  NA     NA    
4 1     coffee    0.185 -0.127 <NA>      <NA>  NA     NA    
5 1     smoking   1.09  -2.52  ->        canc…  1.52  -3.66 
# ℹ 3 more variables: circular <lgl>, label <chr>,
#   path <chr>

coffee_cancer_dag |>
  ggdag_paths()

Closing backdoor paths

We need to account for these open, non-causal paths

Randomization

Stratification, adjustment, weighting, matching, etc.

Identifying adjustment sets

ggdag_adjustment_set(smk_wt_dag)

Identifying adjustment sets

Identifying adjustment sets

library(dagitty)
adjustmentSets(smk_wt_dag)

{ active, age, education, exercise, race, sex, smokeintensity,
  smokeyrs, wt71 }

Your Turn 3

Now that we know the open, confounding pathways (sometimes called “backdoor paths”), we need to know how to close them! First, we’ll ask {ggdag} for adjustment sets, then we would need to do something in our analysis to account for at least one adjustment set (e.g. multivariable regression, weighting, or matching for the adjustment sets).

Use `ggdag_adjustment_set()` to visualize the adjustment sets. Add the arguments `use_labels = "label"` and `text = FALSE`.

Write an R formula for each adjustment set, as you might if you were fitting a model in `lm()` or `glm()`

05:00

Your Turn 3

ggdag_adjustment_set(
  coffee_cancer_dag, 
  use_labels = "label", 
  text = FALSE
)

Your Turn 3

Your Turn 3

cancer ~ coffee + addictive
cancer ~ coffee + smoking

Let’s prove it!

set.seed(1234)
dag_data <- coffee_cancer_dag |> 
  simulate_data(-.6)

Let’s prove it!

dag_data

# A tibble: 500 × 4
   addictive cancer coffee smoking
       <dbl>  <dbl>  <dbl>   <dbl>
 1    0.569   3.11  -0.326  -1.29 
 2    0.411   1.52   0.330  -1.57 
 3    1.20    1.06  -0.557  -2.40 
 4   -0.782  -0.504 -0.148   0.376
 5    0.0357 -0.709 -0.342  -1.53 
 6    1.96    1.05  -1.90   -0.823
 7    1.13    0.211 -0.581  -0.534
 8    0.697   0.892 -1.36   -0.267
 9   -0.779   0.748  0.455   0.302
10   -1.13    0.930  0.568   0.742
# ℹ 490 more rows

Let’s prove it!

Choosing what variables to include

Adjustment sets and domain knowledge

Conduct sensitivity analysis if you don’t have something important

Common trip ups

Using prediction metrics

The 10% rule

Predictors of the outcome, predictors of the exposure

Forgetting to consider time-ordering (something has to happen before something else to cause it!)

Selection bias and colliders (more later!)

Incorrect functional form for confounders (e.g. BMI often non-linear)

Time-ordering

don’t adjust for the future!

Your Turn 4

Recreate the DAG we’ve been working with using time_ordered_coords(), then visualize the DAG. You don’t need to use any arguments for this function, so coords = time_ordered_coords() will do.

Your Turn 4

coffee_cancer_dag_to <- dagify(
  cancer ~ smoking,
  smoking ~ addictive,
  coffee ~ addictive,
  exposure = "coffee",
  outcome = "cancer",
  coords = time_ordered_coords(),
  labels = c(
    "coffee" = "Coffee", 
    "cancer" = "Lung Cancer", 
    "smoking" = "Smoking", 
    "addictive" = "Addictive \nBehavior"
  )
)

#TODO: UPDATE LABELS ARGS
ggdag(coffee_cancer_dag_to, use_labels = "label", text = FALSE)coffee_cancer_dag_to <- dagify(
  cancer ~ smoking,
  smoking ~ addictive,
  coffee ~ addictive,
  exposure = "coffee",
  outcome = "cancer",
  coords = time_ordered_coords(),
  labels = c(
    "coffee" = "Coffee", 
    "cancer" = "Lung Cancer", 
    "smoking" = "Smoking", 
    "addictive" = "Addictive \nBehavior"
  )
)

#TODO: UPDATE LABELS ARGS
ggdag(coffee_cancer_dag_to, use_labels = "label", text = FALSE)

Your Turn 4