Add reference to Causal Inference: A Statistical Learning Approach (#1441)

Author: erikcs

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.

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>
 

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).