Causal Filters
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A filter is said to be causal when its output does not depend on any future inputs.
For example, y(n)=x(n+1) is a non-causal filter because the output anticipates the input one sample into the future
Restriction to causal filters is quite natural when the filter operates in real time. Many digital filters, on the other hand, are implemented on a computer where time is artificially represented by an array index. Thus, noncausal filters present no difficulty in such an off-line situation. It happens that the analysis for noncausal filters is pretty much the same as that for causal filters, so we can easily relax this restriction.
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