From Symptoms to Causes: Causal Hypotheses and Verification

A symptom describes what we see; a causal hypothesis proposes why it happened

A rise in delay, complaints or errors is an observed symptom or pattern. “The rise is caused by message design” or “the system slows in the evening” are causal hypotheses. A useful hypothesis links a possible cause, a mechanism and an observable implication: if factor X increases Y through mechanism M, we should expect effect Z when X is present, or the effect should weaken when X is removed. That structure is testable and avoids diagnosing people.

Test the hypothesis against real rivals

Start with four questions: Did the proposed cause precede the outcome? Do the two vary together in the expected way? Are there plausible rival explanations? Is there a comparison or intervention that can distinguish among them? Temporal order is necessary for many causal claims, but it is not sufficient; many things can change at the same time. At this level, the aim is not philosophical proof of causality but avoiding the jump from coincidence to remedy.

Another comparison may be more revealing: if a group not exposed to the new reminder design shows a similar rise in no-shows, the design hypothesis becomes weaker. If only the exposed group changes while other conditions are reasonably stable, the hypothesis becomes stronger. “Stronger” does not mean “proven beyond doubt”; inference depends on the quality of the comparison and the possibility of other differences.

Cause-generation tools do not prove causes

Repeated “why?” questions, cause-and-effect diagrams, and grouping possible causes by process, technology, environment or inputs are useful for expanding the hypothesis set. They become dangerous if the diagram is treated as evidence. After generating hypotheses, return to data, observation, comparison or an appropriate test. The tool helps avoid missing a rival explanation; verification is what changes the weight of a hypothesis.