使用近端嵌入的传染效应估计
Contagion Effect Estimation Using Proximal Embeddings
AI总结：
针对高维代理变量导致传染效应估计方差大的问题，提出结合变分自编码器与对抗网络的近端嵌入框架，降低维度并平衡处理组，显著提升观测网络数据中估计的准确性与稳定性。
摘要：
传染效应指的是社交网络中同伴行为对个体结果的因果效应。由于潜在的同类相吸（latent homophily）现象，传染可能受到混杂因素的影响，这使得传染效应估计变得非常困难：在同质性网络中，节点倾向于与具有相似属性的同伴建立联系，并且可以在不相互影响的情况下表现出相似的行为。考虑未观测混杂因素的代理变量（proxies）是解决潜在同类相吸的一种方法。然而，正如我们在本文中所展示的，当代理变量是高维时，现有的基于代理的传染效应估计方法具有非常高的方差。为了解决这个问题，我们引入了一个新颖的框架——近端嵌入（Proximal Embeddings, ProEmb），该框架将变分自编码器（VAEs）与对抗网络相结合，以创建高维代理变量的低维表示，并帮助识别传染效应。虽然VAEs此前已用于因果推断中的表示学习，但我们方法的一个新颖之处在于增加了对抗网络组件，以平衡不同处理组的表示，这在从观测数据中进行因果推断时至关重要，因为这些组通常来自不同的分布。我们通过实验表明，与最先进的方法相比，我们的方法显著提高了观测网络数据中传染效应估计的准确性，并降低了方差。
英文摘要：
Contagion effect refers to the causal effect of peers' behavior on the outcome of an individual in social networks. Contagion can be confounded due to latent homophily which makes contagion effect estimation very hard: nodes in a homophilic network tend to have ties to peers with similar attributes and can behave similarly without influencing one another. One way to account for latent homophily is by considering proxies for the unobserved confounders. However, as we demonstrate in this paper, existing proxy-based methods for contagion effect estimation have a very high variance when the proxies are high-dimensional. To address this issue, we introduce a novel framework, Proximal Embeddings (ProEmb), that integrates variational autoencoders with adversarial networks to create low-dimensional representations of high-dimensional proxies and help with identifying contagion effects. While VAEs have been used previously for representation learning in causal inference, a novel aspect of our approach is the additional component of adversarial networks to balance the representations of different treatment groups, which is essential in causal inference from observational data where these groups typically come from different distributions. We empirically show that our method significantly increases the accuracy and reduces the variance of contagion effect estimation in observational network data compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
