Did Babies Build Roads in Europe? Presumed Causation Between Correlated Variables

A frequent problem with interpretation of data in the social sciences and business research is presumed causation between correlated variables. Two variables can exhibit perfect linear correlation yet not be in a cause and effect relationship. Generally, we need to satisfy at least three stipulations to argue for a cause and effect relationship:

1. Temporal precedence -- the cause happens before the effect.
2. Association between the independent variable (i.e., cause) and dependent variable (i.e., effect) – a linear, geometric, exponential, logarithmic, or some other covariation exists.
3. No reasonable alternatives -- upon careful inspection there are no other reasonable explanations for why the cause would result in the effect.
   

For example, between the years of 1945 and 1962, there were dramatic increases in the number of new roads built in Europe and the number of live births in the United States. (Note that I read this comparison somewhere but I do not recall; I use it frequently when teaching undergraduate statistics, because the face absurdity of the comparison makes the lesson easily remembered by students.) Were babies building roads in Europe? Not likely. Were roads in Europe making it possible for more babies to be born in the good 'ole U.S.A.? Not likely. See, variables can be perfectly correlated and probably unrelated. That is, there is no direct relationship between those variables; a confounding, third variable could be related to both of the correlated variables, which we might assign in this case to the drastic social upheaval that occurred during World War II.

In business research, causation and correlation are frequently confused as well. For example, are the dollars invested in showroom inventory the cause of sales revenue at the retail furniture store? Are the dollars of sales revenue generating investments in new showroom inventory? Still, is a third, confounding variable, such as consumer demand, somehow affecting both? In many cases, the discrete causal variable is not being measured, but at least the three stipulations above must be satisfied to argue for causation between any two known variables.

Enterprise Information Systems: The Problem of Integration

The time and attention of humans is required to integrate the information created, analyzed, and stored by departmental functions. Many impediments to accounting information system integration within the enterprise are easily identifiable, the chief of these being the existence of disconnected information systems that are native to individual functional areas of the organization. These native, function-based information systems are not integrated in any automated sense; instead, cross-functional information systems are integrated by the ultimate software system: people.

Of course, the entire organization must grow to survive, and business process growth inevitably requires storage and retrieval of additional information in departmental database servers, the nexus of business process growth. Such inter-departmental integration challenges are common, as managers require performance reporting that reflects a highly fluid business environment. Even the information systems within departmental functions can grow and morph to introduce intra-departmental integration challenges.

Integration can be partially achieved by integrating similar types of systems and finally the reporting output from those systems (Dunn, Cherrington, & Hollander, 2005). Information system planning can reduce the number and scope of information pockets stored in the various functional silos within the business enterprise by building systems from scratch or obtaining enterprise wide accounting systems. The key concept to understand in information system integration is to re-engineer business processes along with concomitant accounting information systems from the ground-up and avoid partial patching of information systems to achieve necessary integration. However, the low hanging fruit in accounting system re-engineering may be simply capturing and recording the same information with shorter elapsed time and fewer inaccuracies, not necessarily re-engineering the entire business process. The trade-offs seem a matter of project scope.

Reference

Dunn, C., Cherrington, J.O., & Hollander. A.S. (2005). Enterprise information systems: A patterned-based approach, 3rd edition. New York: McGraw-Hill/Irwin.