scholarly journals Improving causal inference of mediation analysis with multiple mediators in randomized experiments

2021 ◽  
Author(s):  
Wen Wei Loh ◽  
Dongning Ren
Keyword(s):  
Causal Inference ◽  
Mediation Analysis ◽  
Causal Structure ◽  
Psychology Research ◽  
Direct Effects ◽  
Randomized Experiments ◽  
Causal Inferences ◽  
Mediated Effects ◽  
Causal Assumptions ◽  
Intermediate Variables

Mediation analysis is an essential tool for investigating how a treatment causally affects an outcome via intermediate variables. However, violations of the (often implicit) causal assumptions can severely threaten the validity of causal inferences of mediation analysis. Psychologists have recently started to raise such concerns, but the discussions have been limited to mediation analysis with a single mediator. In this article, we examine the causal assumptions when there are multiple possible mediators. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators. Substantive conclusions using such methods are predicated on stringent assumptions about the underlying causal structure that can be indefensible in practice. Therefore, we recommend that researchers shift focus to mediator-specific indirect effects using a recently proposed framework of interventional (in)direct effects. A vital benefit of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate correctly assuming the underlying causal structure among the mediators. Finally, we provide a practical guide with suggestions to improve the research practice of mediation analysis at each study stage. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

scholarly journals Recommendations for improving causal inference of mediation analysis

2021 ◽  
Author(s):  
Wen Wei Loh ◽  
Dongning Ren
Keyword(s):  
Causal Inference ◽  
Mediation Analysis ◽  
Indirect Effects ◽  
Causal Structure ◽  
Sensitivity Analyses ◽  
Causal Inferences ◽  
Mediated Effects ◽  
Causal Assumptions ◽  
Pre Treatment ◽  
Indispensable Tool

Mediation analysis is an indispensable tool for investigating how a treatment causally affects an outcome via intermediate variables. Recently, there have been increased concerns about the validity of causal inferences in conclusions drawn using mediation analysis. However, the discussions are limited to a single mediator, and importantly, there is a lack of guidelines on substantiating causal inferences. In this article, we first provide a thorough examination of the causal assumptions underpinning mediation analysis. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators and the stringent -- yet often overlooked -- assumptions that predicate valid inference. To mitigate the risk of invalid inference, we introduce an alternative approach focusing on mediator-specific indirect effects. An appealing feature of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate assuming a (correct) causal structure among the mediators. Finally, we provide a practical guide to improve the research practice of mediation analysis. We clarify when mediation analysis is (in)appropriate; when appropriate, we recommend that researchers preregister (i) justifications of the asserted causal relations in their mediation analysis, (ii) pre-treatment confounders, and (iii) the either path- or mediator-specific indirect effects to be investigated. Confounding adjustment when estimating the preregistered indirect effects and sensitivity analyses for unmeasured confounding can fortify causal inferences in the conclusions. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

scholarly journals COVID-19-induced hyperinflammation, immunosuppression, recovery and survival: how causal inference may help draw robust conclusions

RMD Open ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. e001638
Author(s):  
Robert B M Landewé ◽  
Sofia Ramiro ◽  
Rémy L M Mostard
Keyword(s):  
Supportive Care ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Immunosuppressive Treatment ◽  
Experimental Treatment ◽  
Number Needed To Treat ◽  
Mediated Effects ◽  
Period 2 ◽  
Non Linear ◽  
Quasi Experimental

BackgroundThe CHIC study (COVID-19 High-intensity Immunosuppression in Cytokine storm syndrome) is a quasi-experimental treatment study exploring immunosuppressive treatment versus supportive treatment only in patients with COVID-19 with life-threatening hyperinflammation. Causal inference provides a means of investigating causality in non-randomised experiments. Here we report 14-day improvement as well as 30-day and 90-day mortality.Patients and methodsThe first 86 patients (period 1) received optimal supportive care only; the second 86 patients (period 2) received methylprednisolone and (if necessary) tocilizumab, in addition to optimal supportive care. The main outcomes were 14-day clinical improvement and 30-day and 90-day survival. An 80% decline in C reactive protein (CRP) was recorded on or before day 13 (CRP >100 mg/L was an inclusion criterion). Non-linear mediation analysis was performed to decompose CRP-mediated effects of immunosuppression (defined as natural indirect effects) and non-CRP-mediated effects attributable to natural prognostic differences between periods (defined as natural direct effects).ResultsThe natural direct (non-CRP-mediated) effects for period 2 versus period 1 showed an OR of 1.38 (38% better) for 14-day improvement and an OR of 1.16 (16% better) for 30-day and 90-day survival. The natural indirect (CRP-mediated) effects for period 2 showed an OR of 2.27 (127% better) for 14-day improvement, an OR of 1.60 (60% better) for 30-day survival and an OR of 1.49 (49% better) for 90-day survival. The number needed to treat was 5 for 14-day improvement, 9 for survival on day 30, and 10 for survival on day 90.ConclusionCausal inference with non-linear mediation analysis further substantiates the claim that a brief but intensive treatment with immunosuppressants in patients with COVID-19 and systemic hyperinflammation adds to rapid recovery and saves lives. Causal inference is an alternative to conventional trial analysis, when randomised controlled trials are considered unethical, unfeasible or impracticable.

scholarly journals Why Process Matters for Causal Inference

2011 ◽  
Vol 19 (3) ◽  
pp. 273-286 ◽  
Author(s):  
Adam N. Glynn ◽  
Kevin M. Quinn
Keyword(s):  
Causal Inference ◽  
Partial Information ◽  
Explanatory Variable ◽  
Outcome Variable ◽  
Causal Process ◽  
Causal Inferences ◽  
Process Information ◽  
Treatment Assignment ◽  
Causal Assumptions

Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions conditional on the value of a posttreatment variable than they would be able to do unconditionally. As data become available on a posttreatment variable, these conditional causal assumptions become active and information about the effect of interest is gained. This approach is most beneficial in situations where it is implausible to assume that treatment assignment is conditionally ignorable. We illustrate the approach with an example of estimating the effect of election day registration on turnout.

scholarly journals Thinking clearly about causal inferences of politically motivated reasoning: Why paradigmatic study designs often undermine causal inference

2019 ◽  
Author(s):  
Ben M Tappin ◽  
Gordon Pennycook ◽  
David Gertler Rand
Keyword(s):  
Causal Inference ◽  
Behavioral Science ◽  
Motivated Reasoning ◽  
Scientific Understanding ◽  
Randomized Experiments ◽  
Causal Inferences ◽  
Treatment Assignment ◽  
Effective Interventions ◽  
Political Motivation ◽  
Study Designs

A common inference in behavioral science is that people’s motivation to reach a politically congenial conclusion causally affects their reasoning—known as politically motivated reasoning. Often these inferences are made on the basis of data from randomized experiments that use one of two paradigmatic designs: Outcome Switching, in which identical methods are described as reaching politically congenial versus uncongenial conclusions; or Party Cues, in which identical information is described as being endorsed by politically congenial versus uncongenial sources. Here we contend that these designs often undermine causal inferences of politically motivated reasoning because treatment assignment violates the excludability assumption. Specifically, assignment to treatment alters variables alongside political motivation that affect reasoning outcomes, rendering the designs confounded. We conclude that distinguishing politically motivated reasoning from these confounds is important both for scientific understanding and for developing effective interventions; and we highlight those designs better placed to causally identify politically motivated reasoning.

scholarly journals Mendelian randomisation for mediation analysis: current methods and challenges for implementation

2021 ◽  
Author(s):  
Alice R. Carter ◽  
Eleanor Sanderson ◽  
Gemma Hammerton ◽  
Rebecca C. Richmond ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Measurement Error ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Instrumental Variable ◽  
Real Data ◽  
Mendelian Randomisation ◽  
Differential Measurement ◽  
Individual Level ◽  
Differential Measurement Error ◽  
Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

Causal assumptions and causal inference in ecological experiments

2021 ◽  
Author(s):  
Kaitlin Kimmel ◽  
Laura E. Dee ◽  
Meghan L. Avolio ◽  
Paul J. Ferraro
Keyword(s):  
Causal Inference ◽  
Ecological Experiments ◽  
Causal Assumptions

scholarly journals Mediation Analysis With Intermediate Confounding: Structural Equation Modeling Viewed Through the Causal Inference Lens

2014 ◽  
Vol 181 (1) ◽  
pp. 64-80 ◽  
Author(s):  
Bianca L. De Stavola ◽  
Rhian M. Daniel ◽  
George B. Ploubidis ◽  
Nadia Micali
Keyword(s):  
Structural Equation Modeling ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Equation Modeling

scholarly journals Attention controls multisensory perception via 2 distinct mechanisms at different levels of the cortical hierarchy

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001465
Author(s):  
Ambra Ferrari ◽  
Uta Noppeney
Keyword(s):  
Causal Inference ◽  
Causal Structure ◽  
Multisensory Perception ◽  
Spatial Representations ◽  
Perceptual Inference ◽  
Sensory Signals ◽  
Cortical Hierarchy ◽  
Parietal Cortices ◽  
The Brain ◽  
Different Levels

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via 2 distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

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