For nearly a century, investigators in the social sciences have used regression models to deduce cause-and-effect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work-- a principle honored more often in the breach than the observance.

Directed Acyclic Graphs (DAG's) and Error Correction Models (ECM's) are employed to analyze questions of price discovery between spatially separated commodity markets and the transportation market linking them together. Results from our analysis suggest these markets are highly interconnected but it is the inland commodity market that is strongly influenced by both the transportation and commodity export markets. However, the commodity markets affect the volatility of the transportation market over longer horizons. Our results suggest that transportation rates are critical in the price discovery process lending support for the recent development of exchange traded barge rate futures contracts.

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