Adaptive Multi-Source Causal Inference
This addresses data scarcity in causal inference for researchers and practitioners, but it is incremental as it builds on existing multi-source transfer methods.
The paper tackles the problem of data scarcity in causal effect estimation by leveraging additional source datasets with similar causal mechanisms to infer effects in a target population, proposing adaptive transfer factors to control knowledge transfer strength, and shows effectiveness in experiments on synthetic and real-world datasets compared to baselines.
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.