matching vignette

book/articles/gformula.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Causal Inference"
+title: "Causal Inference (G-Computation)"
 ---
 
 This vignette has 3 goals:

book/articles/matching.qmd

@@ -0,0 +1,68 @@
+---
+title: "Matching"
+author: "Vincent Arel-Bundock"
+---
+
+This chapter introduces how to use `marginaleffects` to estimate treatment effects after pre-processing a dataset to achieve better covariate balance. The presentation is very short. Readers who seek a more comprehensive understanding and application of these methods should refer to [Noah Greifer's excellent and detailed work on the topic](https://ngreifer.github.io/) and to the [`MatchIt` package vignettes and website](https://kosukeimai.github.io/MatchIt/) 
+
+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)
+
+To begin, we load libraries and the data from the classic Lalonde experiment:
+
+```{r}
+library("MatchIt")
+library("marginaleffects")
+data("lalonde", package = "MatchIt")
+
+head(lalonde)
+```
+
+We are interested in the treatment effect of the `treat` variable on the `re78` outcome. The `treat` variable is a binary variable indicating whether the individual received job training. The `re78` variable is the individual's earnings in 1978.
+
+
+## Matching
+
+The first step is to pre-process the dataset to achieve better covariate balance. To do this, we use the `MatchIt::matchit()` function and a 1-to-1 nearest neighbor matching with replacement on the Mahaloanobis distance. This function supports many other matching methods, see `?matchit`.
+
+```{r}
+dat <- matchit(
+    treat ~ age + educ + race + married + nodegree + re74 + re75, 
+    data = lalonde, distance = "mahalanobis",
+    replace = TRUE)
+dat <- match.data(dat)
+```
+
+## Fitting
+
+Now, we estimate a linear regression model with interactions between the treatment and covariates. Note that we use the `weights` argument to use the weights supplied by our matching method:
+
+```{r}
+fit <- lm(
+    re78 ~ treat * (age + educ + race + married + nodegree),
+    data = dat,
+    weights = weights)
+```
+
+## 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:
+
+* `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).
+* `wts="weights"` indicates that we want to use the weights supplied by the matching method.
+
+```{r, warning=FALSE}
+avg_comparisons(
+    fit,
+    variables = "treat",
+    newdata = subset(dat, treat == 1),
+    wts = "weights")
+```
+
+## Learn more
+
+[The `MatchIt` vignette titled "Estimating Effects After Matching"](https://kosukeimai.github.io/MatchIt/articles/estimating-effects.html) describes many more options, including different measures of uncertainty (bootstrap, clustering, etc.), different estimands (ATE, etc.), and different strategies for adjustment.

book/utils/_quarto.yml

@@ -48,6 +48,7 @@ book:
       - articles/gam.qmd
       - articles/logit.qmd
       - articles/lme4.qmd
+      - articles/matching.qmd
       - articles/multiple_imputation.qmd
       - articles/mrp.qmd
       - articles/svalues.qmd