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Data purists would rap my knuckles for asking this question and reply, “Never”.

On the other hand, “data sophists” who’re accustomed to lying with Big Data in even more crude ways would wonder, “Duh, they’re the same, no?”

If you don’t belong to either camp, you might pause and wonder if there’s a golden mean between the two extremes. Like me.

Let me use these two examples to get a feel of when correlation can equal causation and when it can’t.

EXAMPLE 1

Correlation: US spending on science, space and technology goes up or down in tandem with suicides by hanging, strangulation and suffocation.

cec01

Source: Spurious Correlations (http://tylervigen.com/)

Causation: If suicides by hanging etc. go up, US spending on science etc. will also go up.

Action: Monitor suicide rate by hanging. If it goes up, release more budget for R&D. If it goes down, downsize R&D.

Even a diehard Data Sophist would intuitively accept that correlation does not equal causation in this case.

EXAMPLE 2

Correlation: Compared to other zip codes, there’s a significantly higher attach rate of business loans with home loans in 23508.

Causation: If home loan goes up in Zip Code 23508, business loans will also go up.

Action: Monitor home loan volume. If it goes up, source additional funds for business loans. If it goes down, release funds earmarked for business loans.

As we saw in What The Obama Credit Card Decline Means For The Future Of Analytics, the correlation made business sense when the bank in question discovered that its business banking and retail banking sales people sat at the same office in Norfolk (zip code 23508), a practice that led to better exchange of market information. Therefore, intuitively, we can agree that correlation could mean causation in this case.

(Notice my frequent use of intuition. It’s intentional: When all the numbers are collected, crunched and visualized, many business decisions are guided by the gut to some extent. At least the heuristic ones like developing a marketing plan, writing a book or recruiting a sales rep.)

*****

To summarize, correlation does not equal causation in the first example and may equal causation in the second one.

By abstracting the basic differences between the two examples, I propose that correlation can equal causation if the following three conditions are met:

  1. The measured variables belong to the same domain
  2. The correlation makes some business sense in itself
  3. The causation can be validated by backtesting with past data.

So, to answer the question posed in the title of this post,

Correlation can equal causation – sometimes!

Ketharaman Swaminathan On May - 1 - 2015

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  • sketharaman

    UPDATE DATED 22-SEP-2016:

    Apropos my statement, “When all the numbers are collected, crunched and visualized, many business decisions are guided by the gut to some extent.”, here are some supporting quotes from the Pricenomics article titled “Why the Father of Modern Statistics Didn’t Believe Smoking Caused Cancer” (https://priceonomics.com/why-the-father-of-modern-statistics-didnt-believe/):

    “Ronald Fisher had devised an ingenious way to separate correlation from cause. But waiting for absolute proof always comes at a price.”

    “Though the randomized control trial is seen as the gold standard for untangling correlation from causation, practicality and ethics often mean we have to make do, says Dennis Cook, a professor of statistics at the University of Minnesota. We make judgment calls.”

  • sketharaman
  • sketharaman

    UPDATE DATED 12 NOVEMBER 2017:

    According to this TechCrunch article, the conventional approach in Big Data seeks causation in insights derived from analysis of data. It’s this obsession for causation before taking action on insights derived from data that has prevented us from realizing the full potential of Big Data. In the alternative approach suggested by the author, you must “let the chips fall where they fall”. In other words, if you use analytics to analyze data, you must let the insights derived from data to guide action by themselves. You should not force down (error-prone) human judgement on the “data-insight-action-value” chain to decide which insight is “valid” and which insight is “invalid”.

    https://techcrunch.com/2017/01/29/why-the-promise-of-big-data-hasnt-delivered-yet/

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