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use ipw() in weighted outcome chapter #326

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Mar 2, 2025
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use ipw() in weighted outcome chapter #326

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37 changes: 16 additions & 21 deletions chapters/11-outcome-model.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -52,12 +52,13 @@ We will use the ATT weights so the analysis matches that for matching above.
#| warning: false
library(propensity)

seven_dwarfs_9_with_ps <-
glm(
propensity_model <- glm(
park_extra_magic_morning ~ park_ticket_season + park_close + park_temperature_high,
data = seven_dwarfs_9,
family = binomial()
) |>
)

seven_dwarfs_9_with_ps <- propensity_model |>
augment(type.predict = "response", data = seven_dwarfs_9)
seven_dwarfs_9_with_wt <- seven_dwarfs_9_with_ps |>
mutate(w_att = wt_att(.fitted, park_extra_magic_morning))
Expand Down Expand Up @@ -249,25 +250,19 @@ svyglm(wait_minutes_posted_avg ~ park_extra_magic_morning, des) |>

### Sandwich estimator that takes into account the propensity score model

The correct sandwich estimator will also take into account the propensity score model.
The `{PSW}` will allow us to do this.
This package has some quirks, for example it doesn't work well with categorical variables, so we need to create dummy variables for `park_ticket_season` to pass into the model.
*Actually, the code below isn't working because it seems there is a bug in the package. Stay tuned!*
The correct sandwich estimator will also take into account the uncertainty in estimating the propensity score model.
`ipw()` will allow us to do this.
To do so, we need to provide both the propensity score model and the outcome model.

```{r}
#| eval: false
library(PSW)
results <- ipw(propensity_model, weighted_mod)
results
```

seven_dwarfs_9 <- seven_dwarfs_9 |>
mutate(
park_ticket_season_regular = if_else(park_ticket_season == "regular", 1, 0),
park_ticket_season_value = if_else(park_ticket_season == "value", 1, 0)
)
psw(
data = seven_dwarfs_9,
form.ps = "park_extra_magic_morning ~ park_ticket_season_regular + park_ticket_season_value + park_close + park_temperature_high",
weight = "ATT",
wt = TRUE,
out.var = "wait_minutes_posted_avg"
)
We can also collect the results in a data frame.

```{r}
results |>
as.data.frame()
```

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