Guessing by Proximity

Opening Scene

Before X-ray imaging and pipe-tracing tools existed, a plumber’s best guess about which pipe fed a leak often came down to proximity: whichever pipe ran closest to the damage was assumed responsible. It’s an understandable heuristic, and it’s wrong often enough that entire walls got opened for nothing. The same “nearest plausible cause” reasoning shaped how humans understood cause and effect broadly, for most of history, well before any rigorous alternative existed.

In Plain English

Folk causal reasoning attributes an outcome to whatever explanation is most immediate, most vivid, or most available — without systematically ruling out alternative explanations. It’s the natural, intuitive default human reasoning falls back on, and it produces a genuinely useful guess often enough to persist, while also producing confidently wrong conclusions often enough to be a real, well-documented problem.

The Old Way

This reasoning pattern shows up across the entire history of human explanation, well before formal causal inference existed:

John Snow’s famous 1854 investigation of a London cholera outbreak initially competed against the era’s dominant “miasma” (bad air) theory, a plausible-sounding but ultimately incorrect causal explanation that persisted for lack of a rigorous alternative.
Historical medicine often attributed disease to whatever was most visible or nearby — bad smells, unlucky locations — rather than the actual, often invisible causal mechanism.
Early economic explanations often attributed events to the most recent, most visible policy change, without ruling out other simultaneous factors.

Snow’s own breakthrough — carefully mapping cholera cases and tracing them to a specific water pump — is itself an early, celebrated example of exactly the rigorous tracing this series covers, done well before formal statistical causal inference existed.

What’s Changing (and Why AI Is the Reason)

Formal causal inference methods, covered throughout the rest of this series, now provide a systematic alternative to folk causal reasoning, letting analysts rule out competing explanations rather than simply picking the most immediately plausible one.
Growing data availability has made it possible to test causal hypotheses rigorously that once could only be argued about informally, the way Snow’s map turned intuition into evidence.
As AI systems increasingly generate their own plausible-sounding explanations for patterns in data, the risk of a new, technologically dressed-up version of folk causal reasoning — a confident-sounding story that hasn’t actually been rigorously tested — has become a genuine, modern concern.

The Metaphor, Fully Extended

Behind the Wall	Folk Causal Reasoning Concept
Assuming the nearest pipe is responsible for a leak	Attributing an outcome to the most immediate, plausible explanation
A plumber who’s occasionally right and often wrong by proximity guessing	Folk reasoning that’s occasionally right and often wrong
John Snow tracing cholera cases to an actual water pump, not the prevailing “bad air” theory	Rigorous causal tracing replacing a plausible but incorrect folk explanation
Modern pipe-tracing tools replacing proximity guessing	Modern causal inference methods replacing folk causal reasoning

For Beginners: What to Actually Do

Learn the story of John Snow’s cholera investigation as a genuine historical example of rigorous causal tracing predating formal statistics.
Practice recognizing folk causal reasoning in your own first instinct about a new pattern, and treat that instinct as a hypothesis, not a conclusion.
Get comfortable with the idea that a “most plausible” explanation still needs rigorous testing before being trusted.

For Practitioners and Leaders: The Deeper Layer

Build organizational habits around treating an intuitively plausible causal story as a hypothesis to test, not a finding to act on.
Recognize AI-generated explanations for patterns in data as a potential new form of folk causal reasoning if they aren’t independently, rigorously verified.
Invest in the rigorous causal methods this series covers specifically as a defense against confidently wrong, intuitively appealing explanations.

Quick Recap

Folk causal reasoning attributes outcomes to the most immediate, plausible explanation, without systematically ruling out alternatives.
This pattern has deep historical roots and persists because it’s occasionally right, even though it’s also often wrong.
John Snow’s cholera investigation is a celebrated early example of rigorous causal tracing replacing a plausible but incorrect folk theory.
AI-generated explanations risk becoming a modern, technologically dressed-up version of the same folk reasoning if not rigorously tested.

Where This Fits in the Series

Article 3 covered the trap rigorous causal inference was built to escape. Article 4 introduces the single most common reason two pipes appear connected when they aren’t: a hidden third pipe feeding both.