Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn

04/17/2019
by   Trang Quynh Nguyen, et al.
0

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 effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this paper is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates two perspectives (or purposes of analysis): 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 leveraging a general class of interventional effects that contains as special cases most of the usual effect types -- interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/26/2020

Identification of causal direct-indirect effects without untestable assumptions

In causal mediation analysis, identification of existing causal direct o...
research
02/10/2023

De-confounding causal inference using latent multiple-mediator pathways

Causal effect estimation from observational data is one of the essential...
research
03/11/2019

Estimating Individual Advertising Effect in E-Commerce

Online advertising has been the major monetization approach for Internet...
research
05/14/2020

Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects

We point out an example of Simpson's paradox in COVID-19 case fatality r...
research
08/30/2023

Hypothesis-driven mediation analysis for compositional data: an application to gut microbiome

Biological sequencing data consist of read counts, e.g. of specified tax...
research
02/14/2020

A general theory of identification

What does it mean to say that a quantity is identifiable from the data? ...

Please sign up or login with your details

Forgot password? Click here to reset