做网络推断、因果发现或传染病建模的研究者终于有了一个不依赖模型假设的通用方法——模拟和真实数据都验证了效果，值得直接复现试试。
许多重要现象（如产品采用、疾病传播、金融风险扩散）以动态级联方式展开，恢复其背后的隐藏影响网络是关键挑战。现有方法通常假设特定的扩散模型，当假设错误时性能大幅下降。CascadeNet 提出基于雅可比矩阵的机器学习框架，无需指定扩散机制，通过一步转移函数的雅可比矩阵刻画影响结构，并利用 Neyman 正交去偏实现统计推断。在九种常见数据生成过程的模拟中，CascadeNet 恢复精度最高；在西班牙 52 省 COVID-19 传播的真实案例中，其恢复的网络与真实人口流动网络显著相关，而基线方法无显著对齐。
Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach
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.