Inference Trees

When defining evaluation criteria with constructed scales, it is often desirable to define discrete levels or categories of impact. These can be qualitative (low, medium, high) or quantitative (0-20 hectares, 20-50 hectares, etc.).

Many assumptions may go into the assignment of these impact levels. Inference trees document explanatory factors used in making an assessment of an impact level, along with the supporting data used. It is a useful format for documenting layers of causal factors that are considered in making judgments. A disadvantage is that this approach lacks a means to represent interactions among causal factors, or a system for quantifying relationships among the factors. (click to enlarge)


Key Ideas

  • Inference trees add rigor and transparency when assigning values to complex constructed scales