  This work presents the problem of learning an unknown von Neumann measurement
of dimension $d$ using indefinite causal structures. In the considered
scenario, we have access to $N$ copies of the measurement. We use formalism of
process matrices to store information about the given measurement, that later
will be used to reproduce its best possible approximation. Our goal is to
compute the maximum value of the average fidelity function $F_d(N)$ of our
procedure. We prove that $F_d(N) = 1 - \Theta \left( \frac{1}{N^2}\right)$ for
arbitrary but fixed dimension $d$. Furthermore, we present the SDP program for
computing $F_d(N)$. Basing on the numerical investigation, we show that for the
qubit von Neumann measurements using indefinite causal learning structures
provide better approximation than quantum networks, starting from $N \ge 3$.
