  Data scarcity is a tremendous challenge in causal effect estimation. In this
paper, we propose to exploit additional data sources to facilitate estimating
causal effects in the target population. Specifically, we leverage additional
source datasets which share similar causal mechanisms with the target
observations to help infer causal effects of the target population. We propose
three levels of knowledge transfer, through modelling the outcomes, treatments,
and confounders. To achieve consistent positive transfer, we introduce
learnable parametric transfer factors to adaptively control the transfer
strength, and thus achieving a fair and balanced knowledge transfer between the
sources and the target. The proposed method can infer causal effects in the
target population without prior knowledge of data discrepancy between the
additional data sources and the target. Experiments on both synthetic and
real-world datasets show the effectiveness of the proposed method as compared
with recent baselines.
