In this figure we see the process of generating universals through empirical evidence and induction. This knowledge is put into causal models (the bridge). Causal models are then used in the interpretation of particulars which are what is faced in clinical practice (each patient is a particular instance of the population). There are problems with applying sample statistics to a single case. With the above representation we do not have to apply sample statistics to a particular. We have used sample statistics to help develop our causal models. We figure out what to do in practice by inference of cause from particular observed effects (abduction) to figure out what the problem is, and then knowing the cause we practice of inference of hoped for effects by manipulation of causes (deduction) for interventions (actions). Knowledge for this process are the causal models which are far more complicated structures than the universals generated from the inductive process. So, with a knowledge based practice, knowledge refers to all parts of this process, it is all knowledge, but that means we are adding the causal model knowledge.
To close this loop a bit, with more complexity to the above graphic we see that particulars, practice, universals and causal models all impact the evidence generation process. They provide the structure which we use to make sense of observations (data) as it is collected, even to organize our collection of data (methods).
How is the causal model knowledge different from the knowledge derived from empirical evidence, the universals? It is far more connected and thus to complex for any study to provide direct empirical evidence to support, or at least direct empirical evidence to support decisions about a particular. The name of this post is “Graphical Causal Models.” In upcoming posts I will be starting to go through the process of demonstrating the usefulness of graphical causal models for generating knowledge for practice. When discussing the case for cause (Part 2) and about models, I mentioned the importance of Judea Pearl’s work to KBP. Here, the use of graphical causal models (particular Directed Acyclic Graphs or “DAGs”) is directly attributed to a study of Pearl’s contribution to causation. Such an approach is increasingly common for causal inference in epidemiology and the social sciences. So the left side of the graphic is quite well-developed. KBP faces the challenge of figuring out the use and benefits of graphical causal models for the right side of the above graphic (from causal models to particulars to practice decisions).
I see three major initiatives coming from this framework.
Articulating general principles and particular examples of the interaction between universals and graphical causal models (left side of the graphic)
Articulating general principles and particular examples of the interaction between graphical causal models and particulars (right side of the graphic)
Articulating the consequences of the above for generating clinical practice guidelines and practice recommendations; clinical education; clinical research and research agendas (entire graphic)
These are bold and will most likely take the rest of my career. I am actively seeking collaborators that would like to start meeting to discuss being involved in the above process. If interested, just let me know: firstname.lastname@example.org