From e06f203b143e48e6480036145cb414454d6661a0 Mon Sep 17 00:00:00 2001
From: Erik Sverdrup <erik.sverdrup@gmail.com>
Date: Sat, 7 Sep 2024 23:11:07 -0700
Subject: [PATCH] Add reference to Causal Inference: A Statistical Learning
 Approach (#1441)

---
 README.md                                   | 4 ++++
 r-package/grf/vignettes/grf_guide.Rmd       | 2 +-
 r-package/grf/vignettes/policy_learning.Rmd | 2 +-
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3a3f795d6..2f3ac56e1 100755
--- a/README.md
+++ b/README.md
@@ -165,6 +165,10 @@ Erik Sverdrup, Maria Petukhova, and Stefan Wager.
 <b>Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial with Causal Forests.</b> 2024.
 [<a href="https://arxiv.org/abs/2409.01578">arxiv</a>]
 
+Stefan Wager.
+<b>Causal Inference: A Statistical Learning Approach.</b> 2024.
+[<a href="https://web.stanford.edu/~swager/causal_inf_book.pdf">pdf</a>]
+
 Stefan Wager and Susan Athey.
 <b>Estimation and Inference of Heterogeneous Treatment Effects using Random Forests.</b>
 <i>Journal of the American Statistical Association</i>, 113(523), 2018.
diff --git a/r-package/grf/vignettes/grf_guide.Rmd b/r-package/grf/vignettes/grf_guide.Rmd
index a6d40bad9..238aa74b1 100644
--- a/r-package/grf/vignettes/grf_guide.Rmd
+++ b/r-package/grf/vignettes/grf_guide.Rmd
@@ -405,7 +405,7 @@ qini.age
 
 * [Machine Learning & Causal Inference: A Short Course](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-) (video lectures)
 
-* [Lecture notes - causal inference PhD course at Stanford](http://web.stanford.edu/~swager/stats361.pdf)
+* [Causal Inference: A Statistical Learning Approach](https://web.stanford.edu/~swager/causal_inf_book.pdf) (book)
 
 <center><img src="https://raw.githubusercontent.com/grf-labs/grf/master/images/logo/grf_leaf_green.png" height="64"></center>
 
diff --git a/r-package/grf/vignettes/policy_learning.Rmd b/r-package/grf/vignettes/policy_learning.Rmd
index 0f7081bb0..24f597ac0 100644
--- a/r-package/grf/vignettes/policy_learning.Rmd
+++ b/r-package/grf/vignettes/policy_learning.Rmd
@@ -81,6 +81,6 @@ values <- aggregate(dr.scores, by = list(leaf.node = node.id),
 print(values, digits = 2)
 ```
 
-For more statistical details please see the chapter on *Policy Learning* in [these lecture notes](https://web.stanford.edu/~swager/stats361.pdf).
+For more statistical details, please see the chapter on *Policy Learning* in [Causal Inference: A Statistical Learning Approach](https://web.stanford.edu/~swager/causal_inf_book.pdf).
 
 [^algo]: A description of the tree search algorithm is given in [this paper](https://joss.theoj.org/papers/10.21105/joss.02232.pdf).