A nontechnical explanation of the counterfactual definition of effect modification and interaction

被引:23
|
作者
Bours, Martijn J. L. [1 ]
机构
[1] Maastricht Univ, Dept Epidemiol, GROW Sch Oncol & Dev Biol, POB 616, NL-6200 MD Maastricht, Netherlands
关键词
Effect modification; Interaction; Causal effects; Counterfactual theory; Additive and multiplicative models; IDENTIFICATION; EPIDEMIOLOGY; PANITUMUMAB; WORD; KRAS;
D O I
10.1016/j.jclinepi.2021.01.022
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Effect modification and interaction are important concepts for answering causal questions about interdependent effects of two (or more) exposures on some outcome of interest. Although conceptually alike and often mistakenly regarded as synonymous, effect modification and interaction actually refer to slightly different concepts when considered from a causal perspective. Their subtle yet relevant distinction lies in how the interplay between exposures is defined and the causal roles attributed to the exposures involved in the effect modification and interaction. To gain more insight into similarities and differences between the concepts of effect modification and interaction, the counterfactual theory of causation, albeit complicated, can be very helpful. Therefore, this article presents a nontechnical explanation of the counterfactual definition of effect modification and interaction. Essentially, effect modification and interaction are reflections of the reality and complexity of multicausality. The causal effect of an exposure of interest often depends on the levels of other exposures (effect modification) or causal effects of other exposures (interaction). Consequently, exposure effects should not be regarded in isolation but in combination. Understanding the underlying principles of effect modification and interaction on a conceptual level enables researchers to better anticipate, detect, and interpret these causal phenomena when setting up, analyzing, and reporting findings of (clinical) epidemiological studies. Effect modification and interaction are not biases to be avoided but properties of causal effects that ought to be unveiled. Hence, evidence for effect modification and interaction needs to be shown in order to delineate in whom and which instances causal effects occur. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
引用
收藏
页码:113 / 124
页数:12
相关论文
共 50 条
  • [1] A nontechnical explanation of the counterfactual definition of confounding
    Bours, Martijn J. L.
    [J]. JOURNAL OF CLINICAL EPIDEMIOLOGY, 2020, 121 : 91 - 100
  • [2] A general explanation of the counterfactual definition of confounding
    Suzuki, Etsuji
    Yamamoto, Michio
    Yamamoto, Eiji
    [J]. JOURNAL OF CLINICAL EPIDEMIOLOGY, 2022, 148 : 189 - 192
  • [3] The Counterfactual Definition of a Program Effect
    Reichardt, Charles S.
    [J]. AMERICAN JOURNAL OF EVALUATION, 2022, 43 (02) : 158 - 174
  • [4] A counterfactual explanation for the action effect in causal judgment
    Henne, Paul
    Niemi, Laura
    Pinillos, Angel
    De Brigard, Felipe
    Knobe, Joshua
    [J]. COGNITION, 2019, 190 : 157 - 164
  • [5] PENSION FUNDING - NONTECHNICAL EXPLANATION
    BERIN, BN
    [J]. COMPENSATION REVIEW, 1972, 4 (03): : 17 - 24
  • [6] Counterfactual Narrative Explanation
    Dohrn, Daniel
    [J]. JOURNAL OF AESTHETICS AND ART CRITICISM, 2009, 67 (01): : 37 - 47
  • [7] A COUNTERFACTUAL THEORY OF CAUSAL EXPLANATION
    RUBEN, DH
    [J]. NOUS, 1994, 28 (04): : 465 - 481
  • [8] Counterfactual Explanation for Fairness in Recommendation
    Wang, Xiangmeng
    Li, Qian
    Yu, Dianer
    Li, Qing
    Xu, Guandong
    [J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2024, 42 (04)
  • [9] A Counterfactual Approach to Explanation in Mathematics
    Baron, Sam
    Colyvan, Mark
    Ripley, David
    [J]. PHILOSOPHIA MATHEMATICA, 2020, 28 (01) : 1 - 34
  • [10] Inference and Explanation in Counterfactual Reasoning
    Rips, Lance J.
    Edwards, Brian J.
    [J]. COGNITIVE SCIENCE, 2013, 37 (06) : 1107 - 1135