{
  "abstract": "In their thoughtful review on causal mediation methods, Pearce et al 1 highlighted the challenges posed by exposure-induced mediator–outcome confounders for identifying natural direct and indirect effects.2 Here, we briefly provide a broader perspective on this issue using causal directed acyclic graphs (DAGs).",
  "authors": [
    {
      "affiliations": [
        "Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan"
      ],
      "name": "Etsuji Suzuki"
    },
    {
      "affiliations": [
        "Interfaculty Initiative in Information Studies, the University of Tokyo, Tokyo, Japan",
        "Department of Biostatistics, School of Public Health, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan"
      ],
      "name": "Tomohiro Shinozaki"
    },
    {
      "affiliations": [
        "Okayama University of Science, Okayama, Japan"
      ],
      "name": "Eiji Yamamoto"
    }
  ],
  "title": "Exposure-induced mediator–outcome confounders in causal mediation: implications and visualisation",
  "uid": "57c90c83-096d-5a43-9a13-5ba5e4237772"
}