  This work considers the problem of transferring causal knowledge between
tasks for Individual Treatment Effect (ITE) estimation. To this end, we
theoretically assess the feasibility of transferring ITE knowledge and present
a practical framework for efficient transfer. A lower bound is introduced on
the ITE error of the target task to demonstrate that ITE knowledge transfer is
challenging due to the absence of counterfactual information. Nevertheless, we
establish generalization upper bounds on the counterfactual loss and ITE error
of the target task, demonstrating the feasibility of ITE knowledge transfer.
Subsequently, we introduce a framework with a new Causal Inference Task
Affinity (CITA) measure for ITE knowledge transfer. Specifically, we use CITA
to find the closest source task to the target task and utilize it for ITE
knowledge transfer. Empirical studies are provided, demonstrating the efficacy
of the proposed method. We observe that ITE knowledge transfer can
significantly (up to 95%) reduce the amount of data required for ITE
estimation.
