If I had to establish causality I understand it is difficulit, whether
conclusion has been derived inductively or deductively.
Please explain and elaborate on the implication of my statement.  

Why is ascribing causality more difficult when conclusion have been
reached through induction.

Correlation does not imply causation.  Illustrate this point with
business examples.

Hello bren-ga,

Establishing causality is difficult in the same way that proving
something in mathematics is difficult.  One must not only show that
both events or conditions occur in conjunction, but must prove that
the presence of one event or condition always causes the other.  In
many cases, some unknown third condition causes both the first two
conditions.  Consider for a moment the electric lightbulb. 
Electricity passed through a coil causes heat; heat causes the
filament to glow and produce light.  One might be tempted to say that
the same is true of gas discharge lamps which produce heat and light
just as incandescents do.  In point of fact however, the electricity
passing through a gas discharge lamp produces both the heat and light
so the two occur simultaneously, but the heat does not cause the
light, nor does the light cause the heat.

In the mathematical sense, induction is sound.  This means that
conclusions based on induction upon true things are provably true.  In
common usage though, induction refers to the process of making a
generalization based on incomplete evidence.  For example, consider a
child learning the meaning of "chair".  A child sees a highchair and
is told this is a chair.  He sees a 4-legged barstool and is told this
is a chair.  He sees a three-legged stool and is told this too is a
chair.  The child assumes (induces) that chairs are flat or gently
curved seats affixed to legs for support.  Then he sees a recliner and
is told this too is a chair - the child must now revise his concept of
a chair in light of new evidence.  Similarly, it may be obvious that
causation is present but new information can reveal this to be mere
coincidence.

I briefly mentioned correlation not implying causation, but you asked
for a business example specifically.  Studies show that certain
reforms precede increased employee morale in 4 out of 5 cases.  An
ex-CEO returns to a company and institutes these reforms.  Shortly
thereafter, surveys of employees indicate that morale has increased
sharply.  Certainly there is correlation, but can we say that the
reforms have caused the increase in morale?  Not so; the CEO is a
popular and dynamic figure whose firing 5 years ago was the subject of
much controversy.  His return caused the increase in morale rather
than his reforms.

If I have been unclear, or if you wanted a real-world rather than
hypothetical example, or are otherwise unsatisfied, please do not
hesitate to request a clarification.

-Haversian

I'm not sure how I'd go with business examples, so I'll just post my
thoughts as a comment. Causality requires logical proof that one thing
causes another, rather than just a correlation between one thing
occuring and another thing occuring.

Example: A man thinks that by throwing his newspaper out the window of
the train each morning he's keeping his city free from tigers. When
someone questions him on this, he says 'but there are no tigers!'.
This is inductive reasoning - I do something with the intention of
making tigers go away, and there are no tigers, therefore it works. As
you can see, the problem is that there may be another event which is
keeping the tigers away (say, a big fence and men with guns), or there
may not be tigers to keep away at all!

Deductive reasoning would go like this (assuming all the premises to
the argument of true, which is not the case here but still...)

1. There are tigers in existence in the region of our city
2. The only way to get rid of them is by throwing newspaper out of the
train window
3. I - and only 1 - throw newspaper out the train window
4. The tigers have gone since I started throwing newspaper out the
train window.

Conclusion: I caused the tigers to go away by throwing newspaper out
of the train window.

A good book on Causality is 

Causality: Models, reasoning, and inference. Judea Pearl. Cambridge
University Press, 2000.

It discusses several paradoxes and so forth about causality, and
develops a Bayesian network based approach to the theory. It turns out
that causality is an idea whose depth and complexity is grossly
underestimated by most people.

I will second the comments on the Pearl book.  

The book is heavy going in places, but fascinating.  

Pearl's home page is here:
http://bayes.cs.ucla.edu/jp_home.html

It has some links to some more information, and some talks that he has given.