Causal Analytics in IIoT – AI That Knows What Causes What, and When
for spurious relationships 18 19 , which is
simply not practicable in most real-world
situations. The position of the authors is that
correlational methods have served well and
are proven to provide useful insights, but are
nonetheless prone to producing spurious
relationships
and
hence
mistaken
20
21
decisions.
outstanding work led him to be awarded in
2012 the industry’s equivalent of the Nobel
Prize, the Turing award, for advances in both
machine learning and causality.
As noted earlier, causality research has been
undertaken
to
develop
different
probabilistic methods and approaches for
identifying cause and effect relationships in
non-experimental or ‘observational’ data. Problems remained however, e.g. how to
identify a causal relationship when unknown
delays occur between cause and effect. And,
what are termed hidden confounders, were
difficult to identify and control for. Earlier,
the work of Weiner (1950s) laid the basis for
several information-theoretic measures of
causality (and for well-known data
compression algorithms).
Causal analytics evolved over the past few
decades from academic studies to practical
solutions such as rCA. A stumbling block
historically in reaching this goal has been to
devise causal algorithms that produce
reliable and accurate results for commercial
and government application. A landmark innovation was that of Clive
Granger 23 , awarded a Nobel prize for
developing a test of causality: X is said to
cause Y, if the past values of X contain
information that helps predict future values
of Y, above and beyond the information
contained in past values of Y – graphically:
A DVANCES IN C AUSAL A NALYTICS AND THE
D EVELOPMENT OF R ELIABLE C AUSAL A NALYTICS ( R CA)
In the 1980s, mathematical advances by
Judea Pearl 22 from UCLA showed that causal
relationships can be represented from data
in terms of probabilities and led him later to
declare that “causality has been
mathematized”. The mathematization was
perhaps a little premature, but Pearl’s
18 https://us.sagepub.com/sites/default/files/upm-binaries/14289_BachmanChapter5.pdf
19 http://www.statisticssolutions.com/establishing-cause-and-effect/
20 https://hbr.org/2015/06/beware-spurious-correlations
21 https://en.wikipedia.org/wiki/Spurious_relationship
22 Judea Pearl https://en.wikipedia.org/wiki/Judea_Pearl
23 Clive Granger https://en.wikipedia.org/wiki/Clive_Granger
IIC Journal of Innovation
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