Transfer Learning for Individual Treatment Effect Estimation
It addresses the challenge of data scarcity in causal inference for researchers and practitioners, though it is incremental as it builds on existing transfer learning methods.
This work tackles the problem of transferring causal knowledge for Individual Treatment Effect (ITE) estimation by introducing a framework with a Causal Inference Task Affinity measure, showing that ITE knowledge transfer can reduce the required data by up to 95%.
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.