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，这些分析效率极为低下。本文提出敏感性感知摊销贝叶斯推理（SA-ABI），这是一种将敏感性分析高效整合到基于神经网络的仿真推理中的多维度方法。首先，我们利用权重共享在训练过程中编码似然函数和先验规范之间的结构相似性，且计算开销极小。其次，我们利用神经网络的快速推理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种似然、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来检测因不可靠近似（例如模型误设导致的）而产生的敏感性。我们在流行病动态、全球变暖阈值及人类决策等应用建模问题中验证了该方法的有效性。结果支持将敏感性感知推理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蔽维度的洞察。