  This paper studies the problem of procuring diverse resources in a forward
market to cover a set $\bf{E}$ of uncertain demand signals $\bf{e}$. We
consider two scenarios: (a) $\bf{e}$ is revealed all at once by an oracle (b)
$\bf{e}$ reveals itself causally. Each scenario induces an optimal procurement
cost. The ratio between these two costs is defined as the {\em price of
causality}. It captures the additional cost of not knowing the future values of
the uncertain demand signal. We consider two application contexts: procuring
energy reserves from a forward capacity market, and purchasing virtual machine
instances from a cloud service. An upper bound on the price of causality is
obtained, and the exact price of causality is computed for some special cases.
The algorithmic basis for all these computations is set containment linear
programming. A mechanism is proposed to allocate the procurement cost to
consumers who in aggregate produce the demand signal. We show that the proposed
cost allocation is fair, budget-balanced, and respects the cost-causation
principle. The results are validated through numerical simulations.
