Novel Approaches to Estimating the Causal Effect of Policy Interventions in the Presence of Spillovers

本研究将提出新的因果推理方法，以估计公共政策干预的溢出效应。政策干预可能会波及到那些没有直接受到政策影响，但却居住在直接受影响的地区附近的人口，如城市或县。不考虑溢出效应可能对公共政策的评价产生严重影响，可能低估或高估政策的总体效果。例如，在一个城市对含糖饮料征税可能会导致饮料饮用者前往附近的城市购买饮料。这可能会破坏评估征税对实施税收的城市饮用含糖饮料的影响的努力。研究人员将调查政策干预在不同模式和邻近地区政策暴露程度下的因果效应。将制定的方法将有助于研究人员和决策者更好地了解在存在溢出效应的情况下政策干预对利益结果的影响。将开办短期课程和讲习班，向更广泛的社区传播新方法。此外，将指导一名研究生，并将开发和提供用户友好的软件。本研究项目将开发一种新的因果估计器，其因果假设比通常用于差异中差异方法的因果假设更宽松。公共政策干预通常采用差异中的差异方法进行评估。然而，这种方法并不能直接解释对邻近地区的溢出效应，例如附近的城市或州。使用新的识别假设，研究人员将开发基于灵活建模和机器学习的双重鲁棒估计器。该项目还将引入一个新的因果被估量，可用于评估各种邻里治疗环境下的政策干预的效果。研究人员将调查识别条件，以确保干预效果是可推广的，并可转移到具有不同组成和邻里环境的目标人群。新方法将用于评估费城饮料税对费城及其周边未实施该税的县销量的影响。这项研究也将为其他考虑征收类似消费税的城市提供指导。这项研究的成果，包括统计软件和实施指南，可供政策制定者用来评估在特定地理区域实施的任何公共政策，并有可能影响其社区。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This research project will produce new causal inference methods to estimate spillover effects of public policy interventions. Policy interventions can spill over to portions of the population who are not directly exposed to the policy, but nonetheless live close to regions, such as cities or counties, that are directly affected. Failure to account for spillover effects can have serious implications on the evaluation of public policies, possibly underestimating or overestimating the overall effects of the policy. For instance, a tax on sugar-sweetened beverages in one city may result in beverage drinkers traveling to a nearby city to purchase beverages. This could undermine efforts to assess the effect of the tax on the drinking of sugar-sweetened beverages in the city that implemented the tax. The researchers will investigate the causal effects of policy interventions under varying patterns and degrees of policy exposure in neighboring regions. The methods to be developed will help researchers and policymakers better understand the effect of policy interventions on outcomes of interest in the presence of spillovers. Short courses and workshops will be developed to disseminate the new methods to the broader community. In addition, a graduate student will be mentored, and user-friendly software will be developed and made available.This research project will develop a novel causal estimator under more relaxed causal assumptions than those commonly used in difference-in-differences approaches. Public policy interventions are commonly evaluated using the difference-in-differences approach. However, this approach does not directly account for spillover effects to neighboring regions, such as nearby cities or states. Using the new identification assumptions, the investigators will develop doubly robust estimators based on flexible modeling and machine learning. The project also will introduce a new causal estimand that can be used to evaluate the effect of a policy intervention under various neighborhood treatment contexts. The researchers will investigate identification conditions that ensure that intervention effects are generalizable and transportable to target populations with different compositions and neighborhood environments. The new methods will be used to assess the impact of the Philadelphia beverage tax on volume sales in Philadelphia and its surrounding counties that did not implement the tax. This research also will provide guidance to other cities considering a similar excise tax. The products of this research, including the statistical software and implementation guidelines, can be used by policy makers to assess any public policy that is implemented in a specific geographic region and has the potential to affect its neighborhoods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.