From knowledge to forecast - An inference map

How do we turn knowledge into forecasts and estimates? Understanding the inference process will make anyone a better analyst. That does not mean that every decision has to be formalized through a methodical approach, but it does mean that analysts should understand the components of good decision-making. 

Good decision-making has been an ongoing goal of this blog as seen through our decision-making entries. The following chart is from The Book of Why by Judea Pearl and Dana Mackenzie and provides a complete formalistic inference approach to decision-making. It is at odds with some of our writing on naturalistic decision-making given the required steps, but for any investment research process, this map provides a good framework.  

Knowledge is hard to explain but includes the complete set of experiences, observations, actions, and morals that we bring to any decision. Obviously, we do not come to a decision with a blank slate but with a set of past information and biases. From this prior knowledge we form a set of assumptions that will be converted into a causal model. The causal model is focused on how one variable will impact another. From this causal model, there can be a set of testable implications. This link with testability is what we should expect from any causal model. The causal model will be able to provide context for a query. 

A question is posed, and the causal model will either be able to answer or not answer the question. If a query cannot be answered by a causal model, then an analyst needs to return to the causal model and adjust to obtain something that is testable. If the query can be answered, there needs to be developed a structure to explain or test the question. This test would represent the statistical estimation used in a research process and will require data input. The test should produce an estimate which will answer the initial query. 

Notice that the inference engine is not the same as a decision framework. The estimate does not say there is an action taken or provides a course for action. The inference engine outlines how we arrive at an estimate to address our query. The key component is that there is a causal model. All estimates have to address the issue of causality which is central to good analysis. Measuring correlation does not provide a framework for testing what can be predictive. Causality drives potential predictions. if we miss on causality, we cannot make good predicts.