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Cause and Effect: How a Revolutionary New Statistical Test Can Tease Them Apart

Posted by timothy on from the submission-caused-post dept.

KentuckyFC writes Statisticians have long thought it impossible to tell cause and effect apart using observational data. The problem is to take two sets of measurements that are correlated, say X and Y, and to find out if X caused Y or Y caused X. That's straightforward with a controlled experiment in which one variable can be held constant to see how this influences the other. Take for example, a correlation between wind speed and the rotation speed of a wind turbine. Observational data gives no clue about cause and effect but an experiment that holds the wind speed constant while measuring the speed of the turbine, and vice versa, would soon give an answer. But in the last couple of years, statisticians have developed a technique that can tease apart cause and effect from the observational data alone. It is based on the idea that any set of measurements always contain noise. However, the noise in the cause variable can influence the effect but not the other way round. So the noise in the effect dataset is always more complex than the noise in the cause dataset. The new statistical test, known as the additive noise model, is designed to find this asymmetry. Now statisticians have tested the model on 88 sets of cause-and-effect data, ranging from altitude and temperature measurements at German weather stations to the correlation between rent and apartment size in student accommodation.The results suggest that the additive noise model can tease apart cause and effect correctly in up to 80 per cent of the cases (provided there are no confounding factors or selection effects). That's a useful new trick in a statistician's armoury, particularly in areas of science where controlled experiments are expensive, unethical or practically impossible.

6 of 137 comments (clear)

  1. No problem. by TechyImmigrant · · Score: 4, Insightful

    >provided there are no confounding factors or selection effects

    So that'll provide plenty of material for medical researchers, nutrition researchers, education researchers and economists to keep doing what they're doing.
     

    --
    I should use this sig to advertise my book ISBN-13 : 978-1501515132.
    1. Re:No problem. by Anonymous Coward · · Score: 5, Insightful

      I can't thing of any cases where I know there are no confounding factors but don't know which is the cause and which is the effect.

      Also, when it comes to medical stuff, or any human observational study, I can't think of any that don't have selection effects as well. Its a neat trick, but I honestly can't think of a single case where it applies in a useful way. Does anyone have an example?

      The article starts with this example of a confounding factor (which makes this test not applicable):

      That turned out to be an erroneous conclusion. Later studies showed that women who took hormone replacement therapy were likely to be from higher socio-economic groups with higher incomes, better diets and generally healthier outcomes. It was this that caused the correlation the earlier studies had found. By contrast, proper randomised controlled trials showed that hormone replacement therapy actually increased the risk of heart disease.

      This test may sometimes be able to provide evidence against causation in such cases (which is useful) but it can't determine causation (because there may be confounding factors). That may be news worthy, but it deserves a more accurate headline: new statistical test can form confidence bounds for how unlikely a it would be for a new parameter to be of this magnitude if there were causation: when combined with existing test it may discredit more potential claims of causation than previously practical.

    2. Re: No problem. by TechyImmigrant · · Score: 4, Insightful

      If you stop the wind all of a sudden, the turbine will continue to turn, causing wind, until the energy in the turbine is spent.

      --
      I should use this sig to advertise my book ISBN-13 : 978-1501515132.
  2. So, correlation CAN mean causation? by Anonymous Coward · · Score: 5, Insightful

    Well, of course it can. How do you think causation is determined? First by noticing a correlation. There can't be causation without correlation.

    Gawd I hate the brain-dead fools who thoughtlessly parrot, "Correlation is not causation!"

  3. Re:David Hume by Black+Parrot · · Score: 5, Insightful

    Yes, but now we can find out whether we read Slashdot because we are nerds, or we are nerds because we read Slashdot.

    --
    Sheesh, evil *and* a jerk. -- Jade
  4. This tells us nothing about the arrow of time by Culture20 · · Score: 3, Insightful

    Which direction in time does cause/effect flow? The world may never know.