CONCEPT
Causal Theory vs Data
Christensen's epistemological commitment: understanding why outcomes occur is more valuable than measuring what outcomes occurred, because data describes only the past, while causal theory can predict novel cases where historical patterns break.
"Data is only available about the past," Christensen wrote. "A useful theory, however, can help you look into the future." The observation places Christensen in direct philosophical tension with the data-driven epistemology that dominates contemporary business analysis and underwrites the architecture of modern AI. Pattern-based predictions extrapolate from historical data; they are reliable only as long as the underlying conditions that produced the correlation remain stable. When conditions change — when a disruption shifts the value network, when a new technology crosses a performance threshold — historical patterns break, and pattern-based prediction fails precisely at the moment accurate prediction matters most. Causal theory identifies the mechanisms that produce outcomes and predicts outcomes in novel circumstances where historical precedent is unavailable.
In The You On AI Field Guide
The distinction matters because patterns and mechanisms produce different kinds of predictions. A pattern-based prediction says: because X has correlated with Y in the past, X will correlate with Y