In network settings, interference between units makes causal inference more challenging as outcomes may depend on the treatments received by others in the network. Typical estimands in network settings focus on treatment effects aggregated across individuals in the population. We propose a framework for estimating node-wise counterfactual means, allowing for more granular insights into the impact of network structure on treatment effect heterogeneity. We develop a doubly robust and non-parametric estimation procedure, KECENI (Kernel Estimation of Causal Effect under Network Interference), which offers consistency and asymptotic normality under network dependence. The utility of this method is demonstrated through an application to microfinance data, revealing the impact of network characteristics on treatment effects.

翻译：在网络环境中，单元间的干扰使得因果推断更具挑战性，因为结果可能依赖于网络中其他单元接收的处理。网络环境中的典型估计量通常关注于跨个体总体的处理效应。我们提出了一个估计节点层面反事实均值的框架，从而能够更精细地洞察网络结构对处理效应异质性的影响。我们开发了一种双重稳健且非参数的估计程序——KECENI（网络干扰下因果效应的核估计），该方法在网络依赖条件下具有一致性和渐近正态性。通过对小额信贷数据的应用，展示了该方法的实用性，揭示了网络特征对处理效应的影响。