  Off-policy evaluation (OPE) is widely applied in sectors such as
pharmaceuticals and e-commerce to evaluate the efficacy of novel products or
policies from offline datasets. This paper introduces a causal deepset
framework that relaxes several key structural assumptions, primarily the
mean-field assumption, prevalent in existing OPE methodologies that handle
spatio-temporal interference. These traditional assumptions frequently prove
inadequate in real-world settings, thereby restricting the capability of
current OPE methods to effectively address complex interference effects. In
response, we advocate for the implementation of the permutation invariance (PI)
assumption. This innovative approach enables the data-driven, adaptive learning
of the mean-field function, offering a more flexible estimation method beyond
conventional averaging. Furthermore, we present novel algorithms that
incorporate the PI assumption into OPE and thoroughly examine their theoretical
foundations. Our numerical analyses demonstrate that this novel approach yields
significantly more precise estimations than existing baseline algorithms,
thereby substantially improving the practical applicability and effectiveness
of OPE methodologies. A Python implementation of our proposed method is
available at https://github.com/BIG-S2/Causal-Deepsets.
