Individualized Policy Evaluation and Learning under Clustered Network Interference
聚类网络干扰下的个体化政策评估与学习AI 解读 ↓
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聚类网络干扰下的个体化政策评估与学习
- 研究问题
- 当同一群组内个体的处理会影响他人结果时，如何有效评估并学习个体化处理规则，同时控制政策学习的遗憾？
- 方法与思路
- 作者采用组内个体处理的加性半参数结果模型，构造政策评估估计量，并为观察研究提出双重稳健版本。
- 材料陈述的贡献
- 摘要称，所提估计量较标准逆概率加权更有效率，并给出有限样本遗憾界及观察研究中的半参数效率结果。
- 阅读关注点
- 应核对加性溢出结构、群组抽样与识别条件，以及效率比较和最优遗憾界依赖的政策类与辅助估计要求。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出完整条件，不能将结论推广至任意网络干扰。
原文摘要
Although there is now a large literature on policy evaluation and learning, much of the prior work assumes that the treatment assignment of one unit does not affect the outcome of another unit. Unfortunately, ignoring interference can lead to biased policy evaluation and ineffective learned policies. For example, treating influential individuals who have many friends can generate positive spillover effects, thereby improving the overall performance of an individualized treatment rule (ITR). We consider the problem of evaluating and learning an optimal ITR under clustered network interference (also known as partial interference), where clusters of units are sampled from a population and units may influence one another within each cluster. Unlike previous methods that impose strong restrictions on spillover effects, such as anonymous interference, the proposed methodology only assumes a semiparametric structural model, where each unit's outcome is an additive function of individual treatments within the cluster. Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR. We show that this estimator is substantially more efficient than the standard inverse probability weighting estimator, which does not impose any assumption about spillover effects. We derive the finite-sample regret bound for a learned ITR, showing that the use of our efficient evaluation estimator leads to the improved performance of learned policies. We consider both experimental and observational studies, and for the latter, we develop a doubly robust estimator that is semiparametrically efficient and yields an optimal regret bound. Finally, we conduct simulation and empirical studies to illustrate the advantages of the proposed methodology.