In signal processing, a causal filter is a linear and time-invariant causal system. The word causal indicates that the filter output depends only on past and present inputs. A filter whose output also depends on future inputs is non-causal, whereas a filter whose output depends only on future inputs is anti-causal. Systems (including filters) that are realizable (i.e. that operate in real time) must be causal because such systems cannot act on a future input. In effect that means the output sample that best represents the input at time comes out slightly later. A common design practice for digital filters is to create a realizable filter by shortening and/or time-shifting a non-causal impulse response. If shortening is necessary, it is often accomplished as the product of the impulse-response with a window function.

An example of an anti-causal filter is a maximum phase filter, which can be defined as a stable, anti-causal filter whose inverse is also stable and anti-causal.

Each component of the causal filter output begins when its stimulus begins. The outputs of the non-causal filter begin before the stimulus begins.


The following definition is a moving (or "sliding") average of input data  . A constant factor of 1/2 is omitted for simplicity:


where x could represent a spatial coordinate, as in image processing. But if   represents time  , then a moving average defined that way is non-causal (also called non-realizable), because   depends on future inputs, such as  . A realizable output is


which is a delayed version of the non-realizable output.

Any linear filter (such as a moving average) can be characterized by a function h(t) called its impulse response. Its output is the convolution


In those terms, causality requires


and general equality of these two expressions requires h(t) = 0 for all t < 0.

Characterization of causal filters in the frequency domainEdit

Let h(t) be a causal filter with corresponding Fourier transform H(ω). Define the function


which is non-causal. On the other hand, g(t) is Hermitian and, consequently, its Fourier transform G(ω) is real-valued. We now have the following relation


where Θ(t) is the Heaviside unit step function.

This means that the Fourier transforms of h(t) and g(t) are related as follows


where   is a Hilbert transform done in the frequency domain (rather than the time domain). The sign of   may depend on the definition of the Fourier Transform.

Taking the Hilbert transform of the above equation yields this relation between "H" and its Hilbert transform:



  • Press, William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, Brian P. (September 2007), Numerical Recipes (3rd ed.), Cambridge University Press, p. 767, ISBN 9780521880688
  • Rowell (January 2009), Determining a System’s Causality from its Frequency Response (PDF), MIT OpenCourseWare