Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning. Implicit learning of causal mechanisms typically involves two categories of interventional data: hard and soft interventions. In real-world scenarios, soft interventions are often more realistic than hard interventions, as the latter require fully controlled environments. Unlike hard interventions, which directly force changes in a causal variable, soft interventions exert influence indirectly by affecting the causal mechanism. However, the subtlety of soft interventions impose several challenges for learning causal models. One challenge is that soft intervention's effects are ambiguous, since parental relations remain intact. In this paper, we tackle the challenges of learning causal models using soft interventions while retaining implicit modelling. We propose ICLR-SM, which models the effects of soft interventions by employing a causal mechanism switch variable designed to toggle between different causal mechanisms. In our experiments, we consistently observe improved learning of identifiable, causal representations, compared to baseline approaches.

翻译：在缺乏已知真实图结构的情况下，从观测数据和干预数据中学习因果表征需要隐式潜在因果表征学习。因果机制的隐式学习通常涉及两类干预数据：硬干预与软干预。在实际场景中，软干预通常比硬干预更为现实，因为后者需要完全受控的环境。与直接强制改变因果变量的硬干预不同，软干预通过影响因果机制间接施加影响。然而，软干预的微妙性给因果模型学习带来了若干挑战。其中一个挑战在于软干预的效果具有模糊性，因为父节点关系保持不变。本文旨在应对使用软干预学习因果模型同时保持隐式建模的挑战。我们提出了ICLR-SM方法，该方法通过设计一个用于在不同因果机制间切换的因果机制开关变量，来建模软干预的效果。在实验中，与基线方法相比，我们持续观察到在可识别因果表征学习方面取得改进。