Correlation, Causation, Confounding, and Spurious Relationships

Install and import#

bash

npm install fintech-algorithms

import { correlationCausationConfoundingAndSpuriousRelationships } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/correlation-causation-confounding-and-spurious-relationships";

Signature#

correlationCausationConfoundingAndSpuriousRelationships(input)

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

input

{
  "x": [1, 2, 3, 4, 5, 6],
  "y": [2, 3, 5, 4, 6, 7],
  "groups": ["A", "A", "A", "B", "B", "B"],
  "predictX": 7,
  "otherPredictor": [2, 4, 5, 8, 9, 13]
}

Call#

correlationCausationConfoundingAndSpuriousRelationships(input)

Returns#

object with 2 fields: rawCorrelation, withinGroupCorrelations

{
  "rawCorrelation": 0.9428571428571428,
  "withinGroupCorrelations": {
    "A": 0.9819805060619656,
    "B": 0.9819805060619659
  }
}

Diagrams#

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

Linear Least Squares Regression — NIST/SEMATECH e-Handbook
Correlation — NIST/SEMATECH e-Handbook
Historical-example decision