Adaptive Multi-Source Causal Inference

TL;DR

This paper introduces an adaptive multi-source causal inference method that leverages additional datasets with similar causal mechanisms to improve causal effect estimation in the target population, using learnable transfer factors for balanced knowledge transfer.

Contribution

It proposes a novel framework with three levels of knowledge transfer and learnable transfer factors, enabling effective causal inference without prior data discrepancy knowledge.

Findings

Outperforms recent baselines on synthetic datasets

Effective in real-world causal inference tasks

Balances transfer strength adaptively

Abstract

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.