  In this paper, we develop {finite-time horizon} causal filters using the
nonanticipative rate distortion theory. We apply the {developed} theory to
{design optimal filters for} time-varying multidimensional Gauss-Markov
processes, subject to a mean square error fidelity constraint. We show that
such filters are equivalent to the design of an optimal \texttt{\{encoder,
channel, decoder\}}, which ensures that the error satisfies {a} fidelity
constraint. Moreover, we derive a universal lower bound on the mean square
error of any estimator of time-varying multidimensional Gauss-Markov processes
in terms of conditional mutual information. Unlike classical Kalman filters,
the filter developed is characterized by a reverse-waterfilling algorithm,
which ensures {that} the fidelity constraint is satisfied. The theoretical
results are demonstrated via illustrative examples.
