  Classical shadow tomography is a sample-efficient technique for
characterizing quantum systems and predicting many of their properties. Circuit
cutting is a technique for dividing large quantum circuits into smaller
fragments that can be executed more robustly using fewer quantum resources. We
introduce a divide-and-conquer circuit cutting method for estimating the
expectation values of observables using classical shadows. We derive a general
formula for making predictions using the classical shadows of circuit fragments
from arbitrarily cut circuits, and provide the sample complexity analysis for
the case when observables factorize across fragments. Then, we numerically show
that our divide-and-conquer method outperforms traditional uncut shadow
tomography when estimating high-weight observables that act non-trivially on
many qubits, and discuss the mechanisms for this advantage.
