Causal Analytics in IIoT – AI That Knows What Causes What, and When
The result is that an observed correlation
over time may or may not be coincidental;
or, the observed correlation (and any
implied causation) may be the result of one
or more third-party variables (hidden
confounders), e.g. another variable that
influences two events that are seemingly
correlated. An example of this may be ice
cream sales and boating accidents that are
correlated, but both are affected by summer
temperatures, and so a causal inference
would be spurious. In this example summer
temperature is causal, but one may
incorrectly infer causation that an increase
in ice cream sales leads to boating accidents
due to the high correlation factor. More
humorous examples of these erroneous
correlations can be found at Spurious
Correlations.
3. ThingWorx's capabilities make it
possible for users to correlate data,
deriving powerful insights… 15
4. Siemens PLM: …quantitative
statistical relationships to real-life
usage, called customer correlation 16
5. Industrial Internet Consortium:
…common issue in IIoT systems is
correlating data between multiple
sensors and process control states 17
Correlational methods are established as
powerful aids to decision-making as is
witnessed in the rise of platforms that
provide the capability. Correlations often
vary such that at a given time one entity and
another may be positively related and at
other times only weakly related or not at all.
There is no fact-based causal coefficient that
describes the strength of potential causal
relationships.
Mathematically-based causal analytics
attempts to improve on correlation for
causality identification.
The lack of stability in correlations indicates
complexity in the relationships and the
presence of a dynamical system (common in
IIoT). This results in variability according to
the system state and nonlinearity in system
behavior. It means that traditional statistical
methods, correlation included, have
limitations for obtaining precise analytics
and improved decision making about
performance in IIoT.
15
The Evolution of Causal Analytics
C AUSALITY FOR R EAL -W ORLD A PPLICATIONS
It is well accepted that causation cannot be
proven statistically unless one conducts an
experiment with randomization to control
A survey of IoT cloud platforms https://www.sciencedirect.com/science/article/pii/S2314728816300149
16
Customer Correlation Durability Methodology
https://www.plm.automation.siemens.com/en/products/lms/engineering/customer-correlation.shtml
17
Industrial Analytics: The Engine Driving the IIoT Revolution https://www.iiconsortium.org/pdf/Industrial_Analytics-
the_engine_driving_IIoT_revolution_20170321_FINAL.pdf
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