标题： 偏倚与高维调整在同伴效应观察性研究中的应用 显示英文标题

标题： Bias and high-dimensional adjustment in observational studies of peer effects

摘要： 同侪效应，即个体行为受到同伴行为影响的理论，在社会科学的多个理论中被提出。 其他过程也可能在网络和群体中产生相关的行径，从而引发关于观察性（即非实验性）研究同侪效应可信度的争论。 然而，识别同侪效应的随机田野实验往往昂贵且难以实施。 因此，许多同侪效应的研究使用观察性数据，而先前评估调整观察性数据以估计同侪效应的因果推断方法的研究缺乏一个实验性的“黄金标准”作为比较。 在这里，我们在 Facebook 上的信息和媒体扩散背景下表明，使用倾向评分模型对非实验对照组（6.77亿个观测值）进行高维调整，得出的同侪效应估计值在统计上与使用大规模随机实验（2.2亿个观测值）得出的结果没有显著差异。 朴素的观察性估计值高估了同侪效应达320%，常用的变量（例如人口统计学特征）提供的偏差修正效果甚微，但调整与目标行为密切相关的先前行为测量值可以减少91%的偏差。 调整超过3700种过去行为的高维模型提供了额外的偏差修正，使得完整模型减少了超过97%的偏差。 这项实验评估表明，个人过去行为的详细记录可以改善社会影响、信息传播和模仿的研究；这些结果对于某些研究的可信度令人鼓舞，但也对罕见或新行为的研究提出了警示。 更普遍地说，这些结果展示了如何利用大规模、高维数据集和统计学习技术来改进行为科学中的因果推断。 显示英文摘要

摘要： Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.