diff --git a/r-package/grf/R/average_treatment_effect.R b/r-package/grf/R/average_treatment_effect.R
index dadc73ba5..a4d612deb 100644
--- a/r-package/grf/R/average_treatment_effect.R
+++ b/r-package/grf/R/average_treatment_effect.R
@@ -29,8 +29,8 @@
 #' assumption, tau(x) is simply the CATE at x. When W is binary
 #' and there are no "defiers", Imbens and Angrist (1994) show that tau(x) can
 #' be interpreted as an average treatment effect on compliers. This function
-#' provides and estimate of tau = E[tau(X)]. See Chernozhukov
-#' et al. (2016) for a discussion, and Section 5.2 of Athey and Wager (2021)
+#' provides an estimate of tau = E[tau(X)]. See Chernozhukov
+#' et al. (2022) for a discussion, and Section 5.2 of Athey and Wager (2021)
 #' for an example using forests.
 #'
 #' If clusters are specified, then each unit gets equal weight by default. For
diff --git a/r-package/grf/man/average_treatment_effect.Rd b/r-package/grf/man/average_treatment_effect.Rd
index 705d6f9a2..f7863fd6c 100644
--- a/r-package/grf/man/average_treatment_effect.Rd
+++ b/r-package/grf/man/average_treatment_effect.Rd
@@ -81,8 +81,8 @@ It can be intepreted causally in various ways. Given a homogeneity
 assumption, tau(x) is simply the CATE at x. When W is binary
 and there are no "defiers", Imbens and Angrist (1994) show that tau(x) can
 be interpreted as an average treatment effect on compliers. This function
-provides and estimate of tau = E[tau(X)]. See Chernozhukov
-et al. (2016) for a discussion, and Section 5.2 of Athey and Wager (2021)
+provides an estimate of tau = E[tau(X)]. See Chernozhukov
+et al. (2022) for a discussion, and Section 5.2 of Athey and Wager (2021)
 for an example using forests.
 
 If clusters are specified, then each unit gets equal weight by default. For