Primer: Causality In Models — Brian Romanchuk

Since is about identifying regularity in change and developing theories that explain the causation, ideally in terms of variables and linear functions. This is a challenge even in complicated simple systems, e.g, in the natural sciences, and it is a huge challenge when dealing with complex adaptive systems in the life and social sciences. 

Complex adaptive systems are synergistic, meaning that they are greater than the sum of the parts, so that examining the parts alone is insufficient for analyzing the system as a whole, that is, the parts and their relationships.

Complex adaptive systems are also subject to emergence, that is, the appearance of properties that are unforeseeable based on analysis of the existing system. For example, systems with the capability to learn from feedback and change behavior based on learning are unpredictable based on discovery of new knowledge and its application. There is as yet no logic of discovery that formalized the process and no scientific theory that has penetrated the causality so to be be able to influence it.

Furthermore, a framework for approaching causality must be assumed and ideally defined operationally in science, as Brian does in this first sentence following:

One important consideration for indicator construction is the notion of causality (using systems engineering terminology). A non-causal model is a model where the output depends upon the future values of inputs. In the absence of access to a time machine, such a model cannot be directly implemented in the real world. In practice, a non-causal model output is “revised” as new datapoints are added to input series. The result is that we cannot use the latest values of the series to judge the quality of previous “predictions” of the model.

The use of non-causal model might be acceptable for the analysis of a historical episode, or an earlier economic regime (such as various Gold Standard periods). Since new data will not arrive, there will be no revisions....

However, causation is still an open question in the philosophy of science.•

Bond Economics
Primer: Causality In Models
Brian Romanchuk

• Causality can be defined as the "causal" connection between cause and effect, e.g., in terms of conditionality (sufficient condition, necessary condition, necessary and sufficient condition). Causality is established though a scientific theory that accounts for the connection, since "correlation is not causation."

Causation is the entire scope of the subject, which includes "causality" as just defined but is not limited to it. There are ontological and epistemology issues regarding causation that are not settled. 

Generally speaking, modern science assumes 1) ontological monism in assuming naturalism, that is, that "everything" can be explained by natural causes as observables (as in a theory of everything). It also assumes 2) epistemological realism in the sense that the mind (subjectivity) is the mirror reality (objectivity) when the scientific method is correctly applied. 

However, these assumptions regarding the framework for gaining knowledge are more presuppositions than stated assumptions. Philosophy of science attempts to bring clarity to this by examining the various issues that arise.