False Causality is correlation does not imply causation. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0297, within the Reasoning family. The core principle: correlation does not imply causation. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Correlation does not imply causation.
Plain-English Definition
Correlation does not imply causation.
Feynman Explanation
Ice cream sales and drowning deaths both rise in summer. Ice cream does not drown people.
Core Principle
Correlation does not imply causation.
Mechanisms
Pending editorial review.
Correlation does not imply causation.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Dashboards create false causality daily.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Ice cream sales and drowning deaths both rise in summer. Ice cream does not drown people.
Examples
- Two metrics move together and are assumed to have a causal link.
- Dashboards create false causality daily.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. The mechanism underneath it is straightforward: correlation does not imply causation. You can recognize it in the field by its signature: ice cream sales and drowning deaths both rise in summer. Ice cream does not drown people. Every element in the Cognition dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, dashboards create false causality daily. It is amplified whenever dashboards create false causality daily. Inside organizations that shows up as dashboards create false causality daily. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to demand a plausible mechanism and test for reverse causality. Design against it the way you would design against a known failure mode — assume it will appear, and price the exploit before someone finds it.
Famous Experiments
Pending editorial review.
Design Principles
- Demand a plausible mechanism and test for reverse causality.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Demand a plausible mechanism and test for reverse causality.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Reasoning that two things are alike based on shallow similarity.
Crediting a source or cause that isn't actually responsible.
Assuming that because B followed A, A caused B.
Presenting two options as the only possibilities when more exist.
Deriving general rules from specific examples; the leap from instance to concept.
The brain evolved to reason adaptively, not always truthfully, to reduce the cost of errors.
We solve problems by adding, even when subtracting would be better.
Assuming that if one option is true, another must be false, when both can be true.
Presuming a purposeful actor behind events that may have no actor at all.
What cannot be settled by experiment is not worth debating.
A finite set of well-defined instructions for solving a problem or performing a computation.
Every model simplifies reality; some are still useful.
Where False Causality is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- EssayGoodhart's Law in the Real World
How measurable proxies capture judgment.
- EssayThe Perverse Incentives Hiding in Your KPIs
Cognitive shortcuts turned into scorecards.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about False Causality
- What is False Causality?
- False Causality is correlation does not imply causation. It sits in the Cognition dimension (COG) of the Human Behavior Taxonomy™ as element HBT-COG-0297, within the Reasoning family. The core principle: correlation does not imply causation. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of False Causality?
- Dashboards create false causality daily. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-COG-0297).
- How is False Causality exploited?
- Dashboards create false causality daily.
- How do you design around False Causality?
- Demand a plausible mechanism and test for reverse causality.
- Which behavioral dimension does False Causality belong to?
- False Causality is classified in the Cognition dimension (COG) of the Human Behavior Taxonomy™, family "Reasoning", class "Mental Model". Its permanent identifier is HBT-COG-0297 and its evidence grade is B.