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Activity Number: 414 - Making Questions Relevant and Assumptions Realistic: New Strategies for Causal Mediation
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Mental Health Statistics Section
Abstract #313232
Title: Clarifying Causal Mediation Analysis for the Applied Researcher: Defining Effects Based on What We Want to Learn
Author(s): Trang Nguyen* and Elizabeth Stuart
Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
Keywords: mediation; effect definitions; effect motivation; estimands; practice

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements -- effect definitions with causal interpretation, clarification of assumptions required for identification, and an expanding array of options for estimation. However, the literature on these results is fast-growing and complex. This paper aims to help ease the understanding and adoption of causal mediation analysis. It explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions. Two perspectives (or purposes of analysis) are differentiated: the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the paper proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types. This class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects.

Authors who are presenting talks have a * after their name.

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