Causal diagrams, causal models

Causal Diagrams: Pitfalls and Tips

I can’t quite piece together the overall links between causal models, causal diagrams, and causal directed acyclic graphs (DAGs) – but there’s an EdX course:

https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your

A very messy wikipedia article

https://en.wikipedia.org/wiki/Causal_model

Some very old university stuff: http://causality.cs.ucla.edu/blog/index.php/about/

pdf paper: causal diagrams in theory and practice

Directed acyclic graphs: a tool for causal studies in paediatrics: https://www.nature.com/articles/s41390-018-0071-3

source

Causal Diagrams: Pitfalls and Tips

J Epidemiol. 2020; 30(4): 153–162.Published online 2020 Apr 5. Prepublished online 2020 Feb 1. doi: 10.2188/jea.JE20190192PMCID: PMC7064555PMID: 32009103

Causal Diagrams: Pitfalls and Tips

Etsuji Suzuki,1Tomohiro Shinozaki,2 and Eiji Yamamoto3Author informationArticle notesCopyright and License informationDisclaimerThis article has been cited by other articles in PMC.Go to:

Abstract

Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) “Non-manipulable” variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.Key words: bias, causal inference, causality, confounding, directed acyclic graphs

continues with full article in source:

Causal Diagrams: Pitfalls and Tips