An introduction to causal mediation analysis

Qin, Xu

doi:10.1007/s12564-024-09962-5

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…Secondly, the mediating effect value for a × b is −0.025, and the value for a × b/c is 0.385 which is less than 1. Also considering that c' is significant, ISU plays a partial mediating role in the process of FI acting on CE [74]. And we measured the mediating effect of ISU as 38.5%.…”

Section: Test For Mediating Effectmentioning

confidence: 99%

How Can Financial Innovation Curb Carbon Emissions in China? Exploring the Mediating Role of Industrial Structure Upgrading from a Spatial Perspective

An,

2024

Sustainability

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Within the sustainability framework, technological innovation’s impact is acknowledged. However, the environmental implications of institutional innovation, a crucial component of the innovation system, remain unclear, necessitating further research. This paper focuses on financial innovation as a representative of institutional innovation, exploring its relationship with carbon emissions. Utilizing panel data from 30 Chinese provinces spanning 2011 to 2022, we establish a spatial Durbin model and a mediating effects model to delve into the intricate relationships among financial innovation, industrial structure upgrading, and carbon emissions. Our findings reveal that: (1) Financial innovation significantly contributes to the upgrading of industrial structures both locally and in neighboring regions; (2) Both financial innovation and industrial structure upgrading effectively mitigate carbon emissions, with the latter playing a mediating role; (3) All three studied factors exhibit spatial clustering effects; (4) The suppressive effect of financial innovation on carbon emissions exhibits a notable spatial spillover. Compared to recent studies, this work innovatively explores the mediating impact mechanism of financial innovation suppressing carbon emissions, particularly demonstrating the spatial spillover characteristics of the mediating effect among the three variables. As China is a major carbon emitter and emerging economy, these insights offer valuable insights for global carbon governance.

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Section: Test For Mediating Effectmentioning

confidence: 99%

How Can Financial Innovation Curb Carbon Emissions in China? Exploring the Mediating Role of Industrial Structure Upgrading from a Spatial Perspective

An,

2024

Sustainability

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“…Finally, the last three articles cover some selected but important topics in causal inference. Li et al (2024) discuss aspects of cluster randomized trials in education research, Qin (2024) provides an introduction to causal mediation analysis, and Shear and Briggs (2024) address measurement issues in causal inference.…”

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confidence: 99%

Causal research designs and analysis in education

Steiner,

Kim

2024

Asia Pacific Educ. Rev.

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Causal inference in education research is crucial for assessing the effectiveness of educational policies, programs, and interventions. Establishing causal relations, that is, identifying interventions that actually work in practice, helps policymakers, educators, and researchers implement strategies that are genuinely beneficial, ensuring resources are allocated efficiently to enhance educational experiences and outcomes. Without rigorous causal inference based on experimental or quasi-experimental designs, efforts to improve education might rely on practices that appear effective but fail to produce actual benefits when scaled or applied in different contexts.The main challenge with causal inference is that data alone are insufficient to reliably assess whether a causal relation between two variables of interest-the treatment conditions and the outcome-exists. In other words, data are uninformative about the causal relation of observed variables (Cunningham, 2021;Pearl & Mackenzie, 2018). Causal inference needs more than data. It also needs reliable * Peter M. Steiner

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Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications

Yin,

Jeong,

Qin

et al. 2024

Sankhya B

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Often linear regression is used to estimate mediation effects. In many instances the underlying relationships may not be linear. Although, the exact functional form of the relationship may be unknown, based on the underlying science, one may hypothesize the shape of the relationship. For these reasons, we develop a novel shape-restricted inference-based methodology for conducting mediation analysis. This work is motivated by an application in fetal endocrinology where researchers are interested in understanding the effects of pesticide application on birth weight, with human chorionic gonadotropin (hCG) as the mediator. Using the proposed methodology on a population-level prenatal screening program data, with hCG as the mediator, we discovered that while the natural direct effects suggest a positive association between pesticide application and birth weight, the natural indirect effects were negative.

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An introduction to causal mediation analysis

Cited by 4 publications

References 64 publications

How Can Financial Innovation Curb Carbon Emissions in China? Exploring the Mediating Role of Industrial Structure Upgrading from a Spatial Perspective

How Can Financial Innovation Curb Carbon Emissions in China? Exploring the Mediating Role of Industrial Structure Upgrading from a Spatial Perspective

Causal research designs and analysis in education

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications

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