The ladder of causality -The basis for any investment analysis and AI

 

The ladder of causality, as presented in the Book of Why, is a simple way of looking at how inference increases in complexity. Using the ladder is a good way for walking through how reasoning can progress from what is easy to more difficult.

On the first rung, there is the activity of seeing. This  reasoning is the power of finding association. Association is not always an easy activity but seeing a relationship may not tell us anything about causality. Association can be simple or may use complex statistical analysis.   

On the second rung, there is the activity of doing or what is called intervention. If I take a specific action, what will happen as a response? There can be a testable link from action, the doing, to effect, the result. This doing can be basis of experimentation. 

The third rung on the ladder of reasoning is associated with counterfactuals or the use of imagination. It involves imagining what would happen under different circumstances. Can I imagine a situation or world that does not exist? This is the realm of theory and provides understanding through generating a narrative that can be generalized. Imagining of counterfactuals can provide hypotheses of what may happen. 

From the post "Causality to machine inference", we can think about what occurs with machine learning as an application of the ladder of causality and reasoning. At first, a machine learning program will look for association. For example, certain types of loans default. What are the characteristics which lead to default? A machine can learn or answer that question from finding associations. Second, intervention is prediction. Given this loan policy, predictions can be made on whether there will be a default. The third and highest form of reasoning will work through a set of what ifs, a causal model.    

A quant modeler will move up and down the ladder of causality as he looks at new data, makes predictions and finally develops a model to describe what can happen in the future. Progress on an investment research project will be based on climbing the ladder of causality.