Reasoning about Interference Between Units: A General Framework

Author:

Bowers Jake,Fredrickson Mark M.,Panagopoulos Costas

Abstract

If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize, let alone execute. In this article, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the “no interference” assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

Publisher

Cambridge University Press (CUP)

Subject

Political Science and International Relations,Sociology and Political Science

Reference65 articles.

1. When considering models that exclude interference, we will write H(yi,zi, wi ) = yi,wi to indicate that the entries zj ϵ z and Wj ϵ w for j ≠ i do not change the potential outcomes for subject i.

2. Note that our example model is not sensitive to the larger structure of the network. For example, a ring network would induce the same effect as a network composed entirely of triangles, as in each case, all nodes have precisely two neighbors. Theories (and models) that reflect larger network structure can be handled by this framework (e.g., Siegel 2009); however, in the interests of simplicity, we restrict our attention to immediate neighbors only in this example.

3. Toward Causal Inference With Interference

4. P-values maximized over a confidence set for the nuisance parameter;Berger;Journal of the American Statistical Association,1994

5. To further aid applied researchers, all simulations in this section and all hypothesis tests in Sections 3 and 4 are based on the development version of our freely available, open-source software package, RItools, available at https://github.com/markmfredrickson/RItools. Appendix A contains annotated code fragments used in this article, and the source code to the entire document is available in the reproduction archive (Fredrickson and Bowers 2012) (http://hdl.handle.net/1902.1/18933).

Cited by 80 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reducing Interference Bias in Online Marketplace Experiments Using Cluster Randomization: Evidence from a Pricing Meta-experiment on Airbnb;Management Science;2024-04-05

2. New Estimands for Experiments with Strong Interference;Journal of the American Statistical Association;2023-10-18

3. Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects;Journal of the Royal Statistical Society Series B: Statistical Methodology;2023-08-19

4. Detecting Interference in Online Controlled Experiments with Increasing Allocation;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

5. Prescriptive process monitoring based on causal effect estimation;Information Systems;2023-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3