Add checkboxes example in motivation

docs/motivation.md

@@ -10,9 +10,9 @@ There are various ways for estimating CATEs, such as [Targeted Maximum Likelihoo
 
 We've found MetaLearners to be a particularly compelling approach for CATE estimation because:
 
-- they are conceptually simple
-- some of them come with strong statistical guarantees, see e.g. [Nie et al. (2019)](https://arxiv.org/pdf/1712.04912.pdf) for the R-Learner or [Kennedy (2023)](https://arxiv.org/abs/2004.14497) for the DR-Learner
-- they rely on existing, arbitrary prediction approaches
+- [x] They are conceptually simple
+- [x] Some of them come with strong statistical guarantees, see e.g. [Nie et al. (2019)](https://arxiv.org/pdf/1712.04912.pdf) for the R-Learner or [Kennedy (2023)](https://arxiv.org/abs/2004.14497) for the DR-Learner
+- [x] They rely on existing, arbitrary prediction approaches
 
 The latter point is particularly important since it implies that battle-tested and production-grade code from existing prediction libraries such as `scikit-learn`, `lightgbm` or `xgboost` can be reused. Given that the field of CATE estimation is still young and engineering efforts limited, this is a highly relevant factor.
 

mkdocs.yml

@@ -69,6 +69,7 @@ nav:
   - Background: background.md
   - FAQ: faq.md
   - Examples:
+      - examples/index.md
       - examples/example_basic.ipynb
       - examples/example_estimating_ates.ipynb
       - examples/example_reuse.ipynb
@@ -91,6 +92,8 @@ markdown_extensions:
   - pymdownx.highlight
   - pymdownx.superfences
   - pymdownx.inlinehilite
+  - pymdownx.tasklist: # Allow for use of check boxes like in gh flavored md
+      custom_checkbox: true
   - pymdownx.arithmatex:
       generic: true