sync

Author: malcolmbarrett

exercises/01-whole-game-exercises.qmd

@@ -90,6 +90,7 @@ smk_wt_dag <- dagify(
   # specify causal question:
   exposure = "qsmk", 
   outcome = "wt82_71",
+  coords = time_ordered_coords(),
   # set up labels:
   # here, I'll use the same variable names as the data set, but I'll label them
   # with clearer names
@@ -113,7 +114,8 @@ smk_wt_dag <- dagify(
     "smokeintensity" = "smoking\nintensity",
     "smokeyrs" = "yrs of\nsmoking"
   )
-) |>tidy_dagitty(layout = "star")
+) |>
+  tidy_dagitty()
 
 smk_wt_dag
 ```
@@ -313,9 +315,13 @@ fit_ipw <- function(split, ...) {
   # calculate inverse probability weights
   .df <- propensity_model |>
     augment(type.predict = "response", data = .df) |>
-    mutate(wts = wt_ate(.fitted, qsmk))
+    mutate(wts = wt_ate(
+      .fitted,
+      qsmk, 
+      exposure_type = "binary"
+    ))
   
-  # fit correctly bootsrapped ipw model
+  # fit correctly bootstrapped ipw model
   lm(wt82_71 ~ qsmk, data = .df, weights = wts) |>
     tidy()
 }

slides/raw/01-causal_modeling_whole_game.qmd

@@ -95,14 +95,6 @@ nhefs_complete_uc |>
 #| fig.width: 9
 library(ggdag)
 # set up DAG
-coords <- time_ordered_coords(
-  list(
-    c("active", "age", "education", "exercise", "race", "sex", "smokeintensity", 
-"smokeyrs", "wt71"),
-    "qsmk",
-    "wt82_71"
-  )
-)
 smk_wt_dag <- dagify(
   # specify causes of quitting smoking and weight gain:
   qsmk ~ sex + race + age + education + 
@@ -112,7 +104,7 @@ smk_wt_dag <- dagify(
   # specify causal question:
   exposure = "qsmk", 
   outcome = "wt82_71",
-  coords = coords,
+  coords = time_ordered_coords(),
   # set up labels:
   # here, I'll use the same variable names as the data set, but I'll label them
   # with clearer names