发表机构
MIT（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究顺序观测设置下结果干扰的因果推断，基于最大伪似然估计提供计算高效方法学习模型参数，在温和假设下建立参数估计非渐近一致性，通过实验和案例研究证明方法有效性。
AI 中文摘要
我们研究顺序观测设置下结果干扰的因果推断。具体而言，考虑N个单元在T个时间步上的二元结果是马尔可夫的情况。每个时间步，N个单元的结果通过伊辛模型捕获依赖关系，每个结果还受外部场影响，该外部场捕获其处理及潜在混杂因素的影响。潜在混杂因素建模为低秩因子结构。我们的数据是来自此高维分布的单个样本。为估计因果量，我们提供基于最大伪似然估计的计算高效方法学习模型参数。在温和假设下，建立参数估计的非渐近一致性，并表明从学习模型采样后能忠实估计因果量。通过合成实验和实际案例研究证明了该方法的有效性。
英文摘要
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also impacted through an external field capturing the effects of its treatment as well as latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudo-Likelihood Estimation (MPLE) for learning the model parameters. Under mild assumptions, we establish non-asymptotic consistency for parameter estimation and show this translates to faithful estimation of causal quantities of interest after sampling from the learned model. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.
