Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient for complex Bayesian models. In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses into simulation-based inference with neural networks. First, we utilize weight sharing to encode the structural similarities between alternative likelihood and prior specifications in the training process with minimal computational overhead. Second, we leverage the rapid inference of neural networks to assess sensitivity to data perturbations and preprocessing steps. In contrast to most other Bayesian approaches, both steps circumvent the costly bottleneck of refitting the model for each choice of likelihood, prior, or data set. Finally, we propose to use deep ensembles to detect sensitivity arising from unreliable approximation (e.g., due to model misspecification). We demonstrate the effectiveness of our method in applied modeling problems, ranging from disease outbreak dynamics and global warming thresholds to human decision-making. Our results support sensitivity-aware inference as a default choice for amortized Bayesian workflows, automatically providing modelers with insights into otherwise hidden dimensions.

翻译：敏感性分析揭示了各种建模选择对统计分析结果的影响。尽管在理论上具有吸引力，但对于复杂贝叶斯模型而言，其效率极低。在这项工作中，我们提出了敏感性感知的摊销贝叶斯推断（SA-ABI），这是一种将敏感性分析高效集成到基于神经网络的模拟推断中的多层面方法。首先，我们利用权重共享在训练过程中以最小的计算开销编码替代似然函数和先验规范之间的结构相似性。其次，我们利用神经网络的快速推断能力来评估对数据扰动和预处理步骤的敏感性。与大多数其他贝叶斯方法不同，这两个步骤都绕过了为每个似然函数、先验或数据集重新拟合模型的高成本瓶颈。最后，我们提出使用深度集成学习来检测由不可靠近似（例如，由于模型误设）引起的敏感性。我们在应用建模问题中展示了我们方法的有效性，这些问题涵盖疾病爆发动力学、全球变暖阈值和人类决策。我们的结果支持将敏感性感知推断作为摊销贝叶斯工作流的默认选择，自动为建模者提供对原本隐藏维度的洞察。