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Inference With Interference Between Units in an fMRI Experiment of Motor Inhibition

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  • Xi Luo
  • Dylan S. Small
  • Chiang-Shan R. Li
  • Paul R. Rosenbaum

Abstract

An experimental unit is an opportunity to randomly apply or withhold a treatment. There is interference between units if the application of the treatment to one unit may also affect other units. In cognitive neuroscience, a common form of experiment presents a sequence of stimuli or requests for cognitive activity at random to each experimental subject and measures biological aspects of brain activity that follow these requests. Each subject is then many experimental units, and interference between units within an experimental subject is, likely, in part because the stimuli follow one another quickly and in part because human subjects learn or become experienced or primed or bored as the experiment proceeds. We use a recent functional magnetic resonance imaging (fMRI) experiment concerned with the inhibition of motor activity to illustrate and further develop recently proposed methodology for inference in the presence of interference. A simulation evaluates the power of competing procedures.

Suggested Citation

  • Xi Luo & Dylan S. Small & Chiang-Shan R. Li & Paul R. Rosenbaum, 2012. "Inference With Interference Between Units in an fMRI Experiment of Motor Inhibition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 530-541, June.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:530-541
    DOI: 10.1080/01621459.2012.655954
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    Cited by:

    1. Georgia Papadogeorgou & Kosuke Imai & Jason Lyall & Fan Li, 2022. "Causal inference with spatio‐temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1969-1999, November.
    2. David Choi, 2017. "Estimation of Monotone Treatment Effects in Network Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1147-1155, July.
    3. L. Liu & M. G. Hudgens & S. Becker-Dreps, 2016. "On inverse probability-weighted estimators in the presence of interference," Biometrika, Biometrika Trust, vol. 103(4), pages 829-842.
    4. Zhao, Yi & Luo, Xi, 2023. "Multilevel mediation analysis with structured unmeasured mediator-outcome confounding," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    5. Yenny Webb-Vargas & Shaojie Chen & Aaron Fisher & Amanda Mejia & Yuting Xu & Ciprian Crainiceanu & Brian Caffo & Martin A. Lindquist, 2017. "Big Data and Neuroimaging," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 543-558, December.
    6. Lan Liu & Michael G. Hudgens, 2014. "Large Sample Randomization Inference of Causal Effects in the Presence of Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 288-301, March.
    7. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.

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