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Home | Events Archive | Measuring Evidence for Mediation in the Presence of Measurement Error

Measuring Evidence for Mediation in the Presence of Measurement Error

  • Series
    BDS Marketing Seminars
  • Speakers
    Thomas Otter (Goethe University Frankfurt, Germany)
  • Field
  • Location
    Business Data Science at Tinbergen Institute, Gustav Mahlerplein 117, Shanghai room
  • Date and time

    September 29, 2021
    12:15 - 13:15

09:00-11:00: Offline meetings with the speaker (Please let us at seminar@businessdatascience.nl know if you would like to meet with the speaker alone or with a few colleagues.)
11:30 Lunch
12:15-13:15 Presentation - Thomas Otter, Goethe (offline)

See also workshop with Thomas Otter (Goethe University Frankfurt, Germany) on September 28 at Vrije Universiteit Amsterdam in our events calendar.

Please send an email to seminar@businessdatascience.nl if you are interested to participate in this seminar (series) and are not yet on the mailing list.

Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). Recent methodological advances made in the field of mediation analysis aim at developing statistically reliable estimates of the indirect effect of the treatment on the outcome. However, what appears to be an indirect effect through the mediator may reflect a data generating process without mediation, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, we develop the insight that a statistically reliable indirect effect combined with strong evidence for conditional independence of treatment and outcome given the mediator is unequivocal evidence for mediation (as the underlying causal model generating the data) into an operational procedure. Our procedure combines Bayes factors as principled measures of the degree of support for conditional independence, i.e., the degree of support for a Null hypothesis, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. We illustrate how our approach facilitates stronger conclusions by re-analyzing a set of published mediation studies. Joint paper with Arash Laghai.

Read full paper here.