Guaranteed prediction products require causal knowledge gained through experiments
Evidence
The result of this conundrum is that the last crucial element in the construction of high-quality prediction products—i.e., those that approximate guaranteed outcomes—depends upon causal knowledge. As Varian says, “If you really want to understand causality, you have to run experiments. And if you run experiments continuously, you can continuously improve your system.” — Shoshana Zuboff, The Age of Surveillance Capitalism, 931
Categorization
- XREF: connects to: Prediction certainty is enhanced by modifying behavior, not just observing it. — Shoshana Zuboff, The Age of Surveillance Capitalism, 625; Big data correlations alone cannot deliver causal knowledge to prediction systems — Shoshana Zuboff, The Age of Surveillance Capitalism, 930; A prediction product’s value grows with its approximation to certainty — Shoshana Zuboff, The Age of Surveillance Capitalism, 304 (+5 more)
Broader Connections
- Natural experimental conditions allow rigorous causal tests of media effects
#exemplifies(0.82) - A prediction product’s value grows with its approximation to certainty
#expands(0.78) — Shoshana Zuboff, The Age of Surveillance Capitalism, 304 - Model choice between causal orderings must rest on theory, not data alone
#expands(0.75) - Counterbalancing trade-offs force experimenters to choose which comparison gets protected from bias
#qualifies(0.72) — Dijk, Pim van, Physiology, Psychoacoustics and…, 3052 - Predictability requires controlling variables, so heterogeneity defies guaranteed outcomes.
#qualifies(0.70) — David Theo Goldberg, The Threat of Race, 1895