Add new list of written learning resources to the docs

README.md

@@ -85,17 +85,6 @@ CausalPy has a broad range of quasi-experimental methods for causal inference:
 | Instrumental variable regression | Addresses endogeneity by using an instrument variable that is correlated with the endogenous explanatory variable but uncorrelated with the error term. Used when explanatory variables are correlated with the error term, providing consistent estimates of causal effects. |
 | Inverse Propensity Score Weighting | Weights observations by the inverse of the probability of receiving the treatment. Used in causal inference to create a synthetic sample where the treatment assignment is independent of measured covariates, helping to adjust for confounding variables in observational studies. |
 
-## Learning resources
-
-Here are some general resources about causal inference:
-
-* The official [PyMC examples gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html) has a set of examples specifically relating to causal inference.
-* Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press.
-* Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton university press.
-* Cunningham, S. (2021). [Causal inference: The Mixtape](https://mixtape.scunning.com). Yale University Press.
-* Huntington-Klein, N. (2021). [The effect: An introduction to research design and causality](https://theeffectbook.net). Chapman and Hall/CRC.
-* Reichardt, C. S. (2019). Quasi-experimentation: A guide to design and analysis. Guilford Publications.
-
 ## License
 
 [Apache License 2.0](LICENSE)

docs/source/knowledgebase/causal_written_resources.md

@@ -0,0 +1,18 @@
+# Written resources on causal inference
+
+Below is a list of written resources (books, blog posts, etc.) that are useful for learning about causal inference.
+
+## Quasi-experiment resources
+
+* Angrist, J. D., & Pischke, J. S. (2009). [Mostly harmless econometrics: An empiricist's companion](https://www.mostlyharmlesseconometrics.com). Princeton university press.
+* Angrist, J. D., & Pischke, J. S. (2014). [Mastering'metrics: The path from cause to effect](https://www.masteringmetrics.com). Princeton University Press.
+* Cunningham, S. (2021). [Causal inference: The Mixtape](https://mixtape.scunning.com). Yale University Press.
+* Huntington-Klein, N. (2021). [The effect: An introduction to research design and causality](https://theeffectbook.net). Chapman and Hall/CRC.
+* Reichardt, C. S. (2019). Quasi-experimentation: A guide to design and analysis. Guilford Publications.
+
+## Bayesian causal inference resources
+* The official [PyMC examples gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html) has a set of examples specifically relating to causal inference.
+
+## General causal inference resources
+
+* [Awesome Causal Inference](https://github.com/matteocourthoud/awesome-causal-inference), a curated list of resources on causal inference, including books, blogs, and tutorials.

docs/source/knowledgebase/index.md

@@ -7,5 +7,5 @@ glossary
 design_notation
 quasi_dags.ipynb
 causal_video_resources
-
+causal_written_resources
 :::