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vincentarelbundock · Oct 20, 2023 · b2354b6 · b2354b6
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diff --git a/book/articles/NEWS.qmd b/book/articles/NEWS.qmd
@@ -367,7 +367,7 @@ New or improved vignettes:
 
 * Hypothesis Tests and Custom Contrasts using the Delta Method: https://marginaleffects.com/articles/hypothesis.html
 * Multiple Imputation: https://marginaleffects.com/articles/multiple_imputation.html
-* Causal Inference with the g-Formula: https://marginaleffects.com/articles/gformula.html
+* Causal Inference with the g-Formula: https://marginaleffects.com/articles/gcomputation.html
  (Thanks to Rohan Kapre for the idea)
 
 Deprecated or renamed arguments:

diff --git a/book/articles/logit.qmd b/book/articles/logit.qmd
@@ -70,7 +70,7 @@ comparisons(
     variables = "tx")
 ```
 
-Population-averaged (aka "marginal") adjusted risk difference ([see this vignette](gformula.html)) can be obtained using the `avg_*()` functions or using the `by` argument:
+Population-averaged (aka "marginal") adjusted risk difference ([see this vignette](gcomputation.html)) can be obtained using the `avg_*()` functions or using the `by` argument:
 
 ```{r}
 avg_comparisons(mod, variables = "tx")

diff --git a/book/articles/matching.qmd b/book/articles/matching.qmd
@@ -9,7 +9,7 @@ The procedure we highlight can be broken down into three steps:
 
 1. Use `MatchIt` to pre-process the data and achieve better covariate balance
 2. Fit a regression model to the outcome of interest
-3. Use `marginaleffects` and [G-Computation](https://marginaleffects.com/articles/gformula.html) to estimate a quantity of interest, such as the Average treatment effect on the treated (ATT)
+3. Use `marginaleffects` and [G-Computation](https://marginaleffects.com/articles/gcomputation.html) to estimate a quantity of interest, such as the Average treatment effect on the treated (ATT)
 
 To begin, we load libraries and the data from the classic Lalonde experiment:
 
@@ -49,7 +49,7 @@ fit <- lm(
 
 ## Quantity of interest
 
-Finally, we use the `avg_comparisons()` function of the `marginaleffects` package to estimate the ATT and its standard error. In effect, this function applies [G-Computation](https://marginaleffects.com/articles/gformula.html) to estimate the quantity of interest. We use the following arguments:
+Finally, we use the `avg_comparisons()` function of the `marginaleffects` package to estimate the ATT and its standard error. In effect, this function applies [G-Computation](https://marginaleffects.com/articles/gcomputation.html) to estimate the quantity of interest. We use the following arguments:
 
 * `variables="treat"` indicates that we are interested in the effect of the `treat` variable.
 * `newdata=subset(dat, treat == 1)` indicates that we want to estimate the effect for the treated individuals only (i.e., the ATT).

diff --git a/book/utils/_quarto.yml b/book/utils/_quarto.yml
@@ -45,7 +45,7 @@ book:
       - articles/equivalence.qmd
       - articles/experiments.qmd
       - articles/gam.qmd
-      - articles/gformula.qmd
+      - articles/gcomputation.qmd
       - articles/ipw.qmd
       - articles/logit.qmd
       - articles/lme4.qmd