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Using {rsample} to bootstrap our causal effect

-
+
# fit ipw model to bootstrapped samples
-ipw_results <- bootstraps(nhefs_complete, 1000, apparent = TRUE) |>
+ipw_results <- bootstraps(nhefs_complete_uc, 1000, apparent = TRUE) |>
   mutate(results = map(splits, fit_ipw))
diff --git a/slides/raw/01-causal_modeling_whole_game.qmd b/slides/raw/01-causal_modeling_whole_game.qmd index 8671875..cff54ff 100644 --- a/slides/raw/01-causal_modeling_whole_game.qmd +++ b/slides/raw/01-causal_modeling_whole_game.qmd @@ -500,7 +500,6 @@ fit_ipw <- function(split, ...) { ```{r} #| label: boot_cache -#| cache: true #| code-line-numbers: "|2-3" # fit ipw model to bootstrapped samples ipw_results <- bootstraps(nhefs_complete_uc, 1000, apparent = TRUE) |> diff --git a/slides/raw/01-causal_modeling_whole_game_cache/html/__packages b/slides/raw/01-causal_modeling_whole_game_cache/html/__packages deleted file mode 100644 index 779f918..0000000 --- a/slides/raw/01-causal_modeling_whole_game_cache/html/__packages +++ /dev/null @@ -1,39 +0,0 @@ -tidyverse -ggplot2 -tibble -tidyr -readr -purrr -dplyr -stringr -forcats -broom -rsample -ggdag -causaldata -Matrix -survival -survey -tableone -haven -usethis -magrittr -smd -devtools -reprex -fs -opensky -shunyata -pak -robustbase 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a/slides/raw/01-causal_modeling_whole_game_cache/html/boot_cache_da8f8cd2769a844813e9ce3f5ec71a80.rdx b/slides/raw/01-causal_modeling_whole_game_cache/html/boot_cache_da8f8cd2769a844813e9ce3f5ec71a80.rdx deleted file mode 100644 index 71ef8a2..0000000 Binary files a/slides/raw/01-causal_modeling_whole_game_cache/html/boot_cache_da8f8cd2769a844813e9ce3f5ec71a80.rdx and /dev/null differ diff --git a/slides/raw/01-causal_modeling_whole_game_cache/revealjs/__packages b/slides/raw/01-causal_modeling_whole_game_cache/revealjs/__packages deleted file mode 100644 index 668f02b..0000000 --- a/slides/raw/01-causal_modeling_whole_game_cache/revealjs/__packages +++ /dev/null @@ -1,18 +0,0 @@ -tidyverse -ggplot2 -tibble -tidyr -readr -purrr -dplyr -stringr -forcats -lubridate -smd -broom -rsample -ggdag -causaldata -propensity -halfmoon -estimatr diff --git a/slides/raw/01-causal_modeling_whole_game_cache/revealjs/boot_cache_c1ae9350162d3ec56b38e7754b023d10.RData 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Simulation

Your Turn 1 (03-ci-with-group-by-and-summarise-exercises.qmd)

Group the dataset by confounder and exposure

Calculate the mean of the outcome for the groups

-

03:00

+

03:00

Your Turn 1

@@ -680,7 +680,7 @@

Your Turn 2

1 -0.0731 -

02:00

+

02:00

Simulation

@@ -818,7 +818,7 @@

Your Turn 3

Use ntile() from dplyr to calculate a binned version of confounder called confounder_q. We’ll create a variable with 5 bins.

Group the dataset by the binned variable you just created and exposure

Calculate the mean of the outcome for the groups

-

03:00

+

03:00

Your Turn 3

diff --git a/slides/raw/04-dags.html b/slides/raw/04-dags.html index 9c48c39..8cc54a0 100644 --- a/slides/raw/04-dags.html +++ b/slides/raw/04-dags.html @@ -526,7 +526,7 @@

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

+

05:00

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

@@ -582,7 +582,7 @@

ggdag_paths()

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

+

05:00

Your Turn 2

@@ -684,7 +684,7 @@

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

+

05:00

Your Turn 3

diff --git a/slides/raw/05-quartets.html b/slides/raw/05-quartets.html index 53f0c7c..8a53a08 100644 --- a/slides/raw/05-quartets.html +++ b/slides/raw/05-quartets.html @@ -393,7 +393,7 @@

Causal inference is not just a statistics problem

-

2023-04-12 (updated: 2023-08-24)

+

2023-04-12 (updated: 2023-08-31)

@@ -524,7 +524,7 @@

For each of the above 4 datasets, fit a linear model to examine the relationship between the exposure and the outcome

-
+
10:00
@@ -557,23 +557,23 @@

Correct effects

-
- @@ -1037,23 +1037,23 @@

Observed effects

-
- @@ -1572,23 +1572,23 @@

The partial solution

-
- @@ -2064,7 +2064,7 @@

On M-Bias

Your turn 2

For each of the following 4 datasets, fit a linear linear model examining the relationship between outcome_followup and exposure_baseline adjusting for covariate_baseline: causal_collider_time, causal_confounding_time, causal_mediator_time, causal_m_bias_time

-

10:00

+

10:00

diff --git a/slides/raw/06-pscores.html b/slides/raw/06-pscores.html index b7432a9..f33be49 100644 --- a/slides/raw/06-pscores.html +++ b/slides/raw/06-pscores.html @@ -499,8 +499,8 @@

Your turn

-

10:00

-

Using the confounders identified in the previous DAG, fit a propensity score model for extra_magic_morning

+

10:00

+

Using the confounders identified in the previous DAG, fit a propensity score model for park_extra_magic_morning

Stretch: Create two histograms, one of the propensity scores for days with extra morning magic hours and one for those without

diff --git a/slides/raw/06-pscores_files/figure-revealjs/unnamed-chunk-5-1.png b/slides/raw/06-pscores_files/figure-revealjs/unnamed-chunk-5-1.png index 6cdf2b9..d3bb9ae 100644 Binary files a/slides/raw/06-pscores_files/figure-revealjs/unnamed-chunk-5-1.png and b/slides/raw/06-pscores_files/figure-revealjs/unnamed-chunk-5-1.png differ diff --git a/slides/raw/07-using-pscores.html b/slides/raw/07-using-pscores.html index 260bb99..b4d920b 100644 --- a/slides/raw/07-using-pscores.html +++ b/slides/raw/07-using-pscores.html @@ -705,7 +705,7 @@

Matching in R (ATM)

Your Turn 1

-
+
10:00
@@ -800,7 +800,7 @@

ATE in R
-
+
10:00
diff --git a/slides/raw/08-pscore-diagnostics.html b/slides/raw/08-pscore-diagnostics.html index e5cfa0a..3ff8ff3 100644 --- a/slides/raw/08-pscore-diagnostics.html +++ b/slides/raw/08-pscore-diagnostics.html @@ -458,7 +458,7 @@

Love plot

Your turn 1

-

10:00

+

10:00

Create a Love Plot for the propensity score weighting you created in the previous exercise

@@ -524,9 +524,9 @@

Weighted ECDF

Your turn 2

-

10:00

-

Create an unweighted ECDF examining the weather_wdwhigh confounder by whether or not the day had Extra Magic Hours.

-

Create a weighted ECDF examining the weather_wdwhigh confounder

+

10:00

+

Create an unweighted ECDF examining the park_temperature_high confounder by whether or not the day had Extra Magic Hours.

+

Create a weighted ECDF examining the park_temperature_high confounder

@@ -558,23 +558,23 @@

-
- diff --git a/slides/raw/09-outcome-model.html b/slides/raw/09-outcome-model.html index d6cace7..2c94dfa 100644 --- a/slides/raw/09-outcome-model.html +++ b/slides/raw/09-outcome-model.html @@ -475,16 +475,16 @@

2. Use {rsample} to bootstrap our causal effect

# A tibble: 1,001 × 2 splits id <list> <chr> - 1 <split [1566/587]> Bootstrap0001 - 2 <split [1566/555]> Bootstrap0002 - 3 <split [1566/590]> Bootstrap0003 - 4 <split [1566/599]> Bootstrap0004 - 5 <split [1566/580]> Bootstrap0005 - 6 <split [1566/574]> Bootstrap0006 - 7 <split [1566/572]> Bootstrap0007 - 8 <split [1566/569]> Bootstrap0008 - 9 <split [1566/562]> Bootstrap0009 -10 <split [1566/581]> Bootstrap0010 + 1 <split [1566/584]> Bootstrap0001 + 2 <split [1566/580]> Bootstrap0002 + 3 <split [1566/560]> Bootstrap0003 + 4 <split [1566/595]> Bootstrap0004 + 5 <split [1566/577]> Bootstrap0005 + 6 <split [1566/544]> Bootstrap0006 + 7 <split [1566/590]> Bootstrap0007 + 8 <split [1566/572]> Bootstrap0008 + 9 <split [1566/575]> Bootstrap0009 +10 <split [1566/584]> Bootstrap0010 # ℹ 991 more rows
@@ -495,8 +495,8 @@

2. Use {rsample} to bootstrap our causal effect

# A tibble: 2 × 5
   term        estimate std.error statistic  p.value
   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
-1 (Intercept)     1.83     0.288      6.34 2.93e-10
-2 qsmk            3.44     0.407      8.44 7.05e-17
+1 (Intercept) 1.54 0.294 5.26 1.65e- 7 +2 qsmk 3.39 0.413 8.20 4.92e-16
@@ -542,8 +542,8 @@

3. Pull out the causal effect

Your Turn

-

12:00

-

Create a function called ipw_fit that fits the propensity score model and the weighted outcome model for the effect between extra_magic_morning and avg_spostmin

+

12:00

+

Create a function called ipw_fit that fits the propensity score model and the weighted outcome model for the effect between park_extra_magic_morning and wait_minutes_posted_avg

Using the bootstraps() and int_t() functions to estimate the final effect.

diff --git a/slides/raw/10-continuous-g-comp.html b/slides/raw/10-continuous-g-comp.html index 57db30a..1d03482 100644 --- a/slides/raw/10-continuous-g-comp.html +++ b/slides/raw/10-continuous-g-comp.html @@ -3539,7 +3539,7 @@

Do posted wait times at 8 am affect actual wait times at 9

Your Turn

Work through Your Turns 1-3 in 10-continuous-g-computation-exercises.qmd

-

10:00

+

10:00

diff --git a/slides/raw/11-tipr.html b/slides/raw/11-tipr.html index ad932c5..3f4e9d1 100644 --- a/slides/raw/11-tipr.html +++ b/slides/raw/11-tipr.html @@ -393,7 +393,7 @@

Tipping Point Sensitivity Analyses

-

2021-09-01 (updated: 2023-08-24)

+

2021-09-01 (updated: 2023-08-31)

@@ -678,7 +678,7 @@

Nothing is known

Disney Data

tip_coef()

    -
  • effect_observed: observed exposure - outcome effect 4.45 minutes (95% CI: 0.17, 8.37)
    • +
  • effect_observed: observed exposure - outcome effect 4.32 minutes (95% CI: 0.0009, 8.36)
@@ -695,52 +695,17 @@

tip_coef()

  • confounder_outcome_effect: relationship between the unmeasured confounder and outcome
    • -
    -

    Disney Data

    -

    tip_coef(): specify one, it will estimate the other

    -
    -
      -
    • exposure_confounder_effect
      • -
    • confounder_outcome_effect
      • -
    -
    -
    -
    -

    Disney Data

    -

    Our causal effect estimate: 4.45 minutes (95% CI: 0.17, 8.37)

    -
    -
    -
    -
    # A tibble: 1 × 4
-  effect_observed exposure_confounder_effect confounder_outcome_effect
-            <dbl>                      <dbl>                     <dbl>
-1            0.17                        0.1                       1.7
-# ℹ 1 more variable: n_unmeasured_confounders <dbl>
    -
    -
    -
    -
    -
    library(tipr)
-tip_coef(
-  effect_observed = 0.17,
-  exposure_confounder_effect = 0.1
-)
    -
    -
    - -
    -

    Your turn

    -
    +
    05:00
    -

    Use the tip_coef() function to conduct a sensitivity analysis for the estimate from your previous exercises.

    +

    Use the tip_coef() function to conduct a sensitivity analysis for the estimate from your previous exercises. Use the lower bound of the confidence interval for the effect and 0.1 for the exposure-confounder effect.

    diff --git a/slides/raw/13-bonus-selection-bias.html b/slides/raw/13-bonus-selection-bias.html index 4cd412c..7c5e774 100644 --- a/slides/raw/13-bonus-selection-bias.html +++ b/slides/raw/13-bonus-selection-bias.html @@ -470,7 +470,7 @@

    Your Turn

    Work through Your Turns 1-3 in 13-bonus-selection-bias.qmd

    -

    10:00

    +

    10:00

    diff --git a/slides/raw/14-bonus-continuous-pscores.html b/slides/raw/14-bonus-continuous-pscores.html index 3676de9..dcfb00e 100644 --- a/slides/raw/14-bonus-continuous-pscores.html +++ b/slides/raw/14-bonus-continuous-pscores.html @@ -520,18 +520,18 @@

    Do posted wait times at 8 am affect actual wait times at 9

    Your Turn 1

    -

    Fit a model using lm() with avg_spostmin as the outcome and the confounders identified in the DAG.

    +

    Fit a model using lm() with wait_minutes_posted_avg as the outcome and the confounders identified in the DAG.

    Use augment() to add model predictions to the data frame

    -

    In wt_ate(), calculate the weights using avg_postmin, .fitted, and .sigma

    -

    05:00

    +

    In wt_ate(), calculate the weights using wait_minutes_posted_avg, .fitted, and .sigma

    +

    05:00

    Your Turn 1

    post_time_model <- lm(
-  avg_spostmin ~
-    close + extra_magic_morning + 
-    weather_wdwhigh + wdw_ticket_season, 
+  wait_minutes_posted_avg ~
+    park_close + park_extra_magic_morning + 
+    park_temperature_high + park_ticket_season, 
   data = wait_times
 )
    @@ -542,7 +542,7 @@

    Your Turn 1

    wait_times_wts <- post_time_model |>
   augment(data = wait_times) |>
   mutate(wts = wt_ate(
-    avg_spostmin, .fitted, .sigma = .sigma
+    wait_minutes_posted_avg, .fitted, .sigma = .sigma
   ))

    @@ -578,7 +578,7 @@

    Stabilizing extreme weights

    Your Turn 2

    Re-fit the above using stabilized weights

    -

    03:00

    +

    03:00

    Your Turn 2

    @@ -586,7 +586,7 @@

    Your Turn 2

    wait_times_swts <- post_time_model |>
   augment(data = wait_times) |>
   mutate(swts = wt_ate(
-    avg_spostmin, 
+    wait_minutes_posted_avg, 
     .fitted,
     .sigma = .sigma,
     stabilize = TRUE
@@ -621,24 +621,24 @@ 
    Fitting the outcome model
    
 
    
 
    Your Turn 3
    
 
    Estimate the relationship between posted wait times and actual wait times using the stabilized weights we just created.
    
-
     
     03:00 
    
+
     
     03:00 
    
 
    
 
    
 
    Your Turn 3
    
 
    
 
    lm(
-  avg_sactmin ~ avg_spostmin, 
+  wait_minutes_actual_avg ~ wait_minutes_posted_avg, 
   weights = swts, 
   data = wait_times_swts
 ) |>
   tidy() |>
-  filter(term == "avg_spostmin") |>
+  filter(term == "wait_minutes_posted_avg") |>
   mutate(estimate = estimate * 10)
    
 
    
 
    # A tibble: 1 × 5
-  term         estimate std.error statistic   p.value
-  <chr>           <dbl>     <dbl>     <dbl>     <dbl>
-1 avg_spostmin    -3.47    0.0836     -4.15 0.0000805
    
+  term                  estimate std.error statistic p.value
+  <chr>                    <dbl>     <dbl>     <dbl>   <dbl>
+1 wait_minutes_posted_…     2.39    0.0659      3.63 4.93e-4