Causal Analytics in IIoT – AI That Knows What Causes What, and When
ensure that such problems are addressed
correctly and urgently. the sole cause, nor does it always cause the
effect.
Correlation of events and systems is often a
starting point for problem-solving in
industrial environments but “correlation is
not causation” 12 . Correlation helps to point
the way, helps indicate what might be
candidate causative or driving factors for
some particular effect yet keeping in mind
that correlation is simply a measure of
association not causation. Correlations can be misleading. Valuable
results and insights are often found, but the
correlation methods upon which decisions
are made mean that risks are inherent and
could lead to mistaken or sub-optimal
decision-m aking and outcomes.
The chief analytics tool of most industrial IoT
analytics vendors is correlation. Most
sensor-driven data (IoT and machine-
generated logs) is analyzed using a proven
but older form of statistical methods (even
when operating within a machine learning
framework). Correlational methods are the
dominant form of analytics.
Introductory statistics courses tell us that it
is not possible to prove causation unless one
conducts an experiment whereby treatment
and control groups are randomized.
This is totally correct but is just not feasible
to conduct an experiment in 99% of real-
world situations. Algorithmic methods have
a probabilistic and contributory approach –
spurred on by big data’s need for
empirically-based data-driven decisions – to
answering questions about what caused
what or will. For example, a causal
coefficient of 0.83 of X as a causative
influence on Y, does not mean that X is
necessarily the sole cause of Y (there may be
multiple causes) nor does it always cause Y.
X is identified however as a contributory
cause of Y. Similarly, smoking is a
contributory cause of lung cancer; it is not
12
Some examples from IoT vendor
publications and websites demonstrate this
approach:
1. Cisco (Attaining IoT Value): …enable
the company’s customers to perform
real-time data correlation and, as a
result, quickly react to irregularities 13
2. Huawei (‘The IoT's Potential for
Transformation’): …enables
correlation-based process and
productivity improvements. 14
Correlation does not imply causation https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation
13
Attaining IoT Value: How To Move from Connecting Things to Capturing Insights
https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/iot-data-analytics-white-paper.PDF
14
The IoT’s Potential for Transformation http://e.huawei.com/en-
sa/publications/global/ict_insights/201703141505/focus/201703141643
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