cause-effect-graphing

name

cause-effect-graphing

description

Build a directed graph connecting causes (inputs, preconditions) to effects (outputs, actions) through AND/OR/NOT logic nodes, then mechanically derive a decision table from it — for when conditional logic is too tangled to see clearly in prose or needs to be communicated visually to non-technical stakeholders. Use when the user has complex validation logic with nested conditions (more than ~5 interacting conditions), says the spec seems to have implicit logical structure they can't quite see, or needs to communicate business rules to stakeholders via a diagram. For straightforward conditional logic with 5 or fewer conditions, the decision-tables skill is the faster, more direct path to the same result — reach for this only when the graph itself adds clarity.

Cause-Effect Graphing

A cause-effect graph is a directed graph that maps causes (inputs, conditions, preconditions) to effects (outputs, outcomes, system actions) through intermediate logical nodes (AND, OR, NOT). It captures the same information as a decision table but makes the causal structure visible — which is valuable when the logic is complex and non-obvious.

Cause-effect graphing was developed as a precursor to decision table derivation: build the graph first, then mechanically derive the decision table from it.

In practice: for most software problems, go straight to a decision table (see the decision-tables skill). Use cause-effect graphing when:

1. The logical structure between causes is complex (nested ANDs and ORs) and validating the structure before building the table adds value
2. Communicating to stakeholders who benefit from seeing the causal chain visually

When to use

• Complex validation logic with nested conditions
• Communicating business rules to non-technical stakeholders via diagrams
• When the spec seems to have implicit logical structure that isn't visible in prose form
• As a precursor to decision table derivation when conditions > 5 and interactions are complex