Hands-On Machine Learning for Algorithmic Trading

Causal inference

Causal inference aims to identify relationships so that certain input values imply certain outputs—for example, a certain constellation of macro variables causing the price of a given asset to move in a certain way, assuming all other variables remain constant.

Statistical inference about relationships among two or more variables produces measures of correlation that can only be interpreted as a causal relationship when several other conditions are met—for example, when alternative explanations or reverse causality has been ruled out. Meeting these conditions requires an experimental setting where all relevant variables of interest can be fully controlled to isolate causal relationships. Alternatively, quasi-experimental ...