Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central challenge is to recover the hidden influence network behind these cascades. Existing methods typically assume a specific diffusion model, and their performance degrades substantially when that assumption is misspecified. We propose CascadeNet, a Jacobian-based machine learning framework for network recovery that does not require specifying a diffusion mechanism. The key idea is that the underlying influence structure can be characterized by the Jacobian of the one-step transition function. CascadeNet first constructs a flexible estimator of the transition function, and further applies Neyman-orthogonal debiasing via the Riesz representer, so that the debiased Jacobian is $\sqrt{n}$-consistent and asymptotically normal, enabling formal inference on the network structure. We validate CascadeNet in both a simulation exercise and a real-world empirical application. In simulations, where the data-generating process is known, CascadeNet achieves the highest network recovery accuracy across nine common data-generating processes. In an empirical application to COVID-19 transmission across Spain's 52 provinces, CascadeNet recovers transmission networks that are significantly correlated with the true inter-province mobility network, whereas networks recovered by baseline methods show no significant alignment with the ground truth.

翻译：许多重要的结果以动态级联的形式展开，包括产品采用、疾病传播、金融困境和信息扩散。一个核心挑战是恢复这些级联背后的隐藏影响网络。现有方法通常假设特定的扩散模型，当该假设被错误指定时，其性能会大幅下降。我们提出CascadeNet，一种基于Jacobian的机器学习框架，用于网络恢复，无需指定扩散机制。其核心思想在于，潜在影响结构可以通过一步转移函数的Jacobian进行刻画。CascadeNet首先构建转移函数的灵活估计量，随后通过Riesz表示子应用Neyman正交去偏，使得去偏后的Jacobian满足$\sqrt{n}$一致性和渐近正态性，从而能够对网络结构进行形式化推断。我们在模拟实验和真实世界应用中对CascadeNet进行了验证。在模拟中，当数据生成过程已知时，CascadeNet在九种常见数据生成过程中实现了最高的网络恢复精度。在针对西班牙52个省份的新冠病毒传播实证应用中，CascadeNet恢复的传播网络与真实的省际移动网络显著相关，而基线方法恢复的网络与真实情况无显著一致性。