If you are interested in talking more about this kind of modelling, please feel free to reach out to any of the facilitators.
Science before statistics - great talk by Richard McElreath
EdX course on Drawing causal assumptions
The examples in the companion github page for this workshop may be the best place to start.
Guide to the bnlearn package for structure learning in R
The CausalNex Python library that implements the NOTEARS structure learning algorithm
The DAGitty package and tutorials, in R
The DoWhy Python library and it's fantastic support documentation
Thinking with Causal Models: A visual formalism for collaboratively crafting assumptions (Hicks et. al, 2022)
Causal Reasoning with Causal Graphs for Educational Research (Weidlich et al., 2023)
Using causal models to bridge the divide between big data and educational theory (Kitto et al., 2023)
Why the Future of AIED is Causal: Arguments for Creating a Tradition Based on Causal Thinking. (Cohausz., 2025)
Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data (Lübke et al., 2020)
Thinking clearly about correlations and causation: Graphical causal models for observational data (Rohrer, 2018)
A Crash Course in Good and Bad Controls (Cinelli et al., 2022)
What is a causal graph? (Dawid, 2024)
Causal Inference and Bias in Learning Analytics: A Primer on Pitfalls using Directed Acyclic Graphs (Weidlich et al., 2022)
The teacher, the physician and the person: exploring causal connections between teaching performance and role model types using directed acyclic graphs (Boerebach et al., 2013)
Causal variables in the community of inquiry: Creating a directed acyclic graph of the effectiveness of the Philosophy for Children program (Mikkola et al., 2024)
A systematic method for hypothesis synthesis and conceptual model development (Grames et al., 2022)
Systems mapping: How to build and use causal models of systems (Barbrook-Johnson & Penn, 2022)
DAGs with NO TEARS: Continuous Optimization for Structure Learning (Zheng et al., 2018)