A causal filter uses only present and past input values to produce its current output. The trailing moving average is causal; a centered average that uses future samples is not. Future samples can be available when processing a finished recording, but they are unavailable to a live measurement at the current time.
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A causal filter is a type of filter used in signal processing that responds only to current and past input values, meaning it does not have any dependency on future input values. This characteristic makes causal filters particularly suitable for real-time applications where future data is not available for processing. Causality is important in many applications, such as audio and video processing, control systems, and communication systems, where real-time processing is critical.