Adaptive Multi-Source Causal Inference from Observational Data
Published in 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022
Abstract: We propose a new approach to estimate causal effects from observational data. We leverage multiple data sources which share similar causal mechanisms with the scarce target observations to help infer causal effects in the target domain. The data sources may be available in sequence or some unplanned order. Causal inference can be carried out without prior knowledge of the data discrepancy between the source and target observations. We introduce three levels of knowledge transfer through modelling the outcomes, treatments, and confounders to achieve consistent positive transfer. We incorporate parametric transfer factors to adaptively control the transfer strength, thus achieving a fair and balanced knowledge transfer between the sources and the target. We also empirically show the effectiveness of the proposed method as compared with recent baselines.