Communicating causality

European Journal of Epidemiology, Oct 2015

Sonja A. Swanson

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Communicating causality

the ubiquity and importance of causal dia- grams within epidemiology is evidenced by four articles presented in this issue of the European Journal of Epi Sonja A. Swanson 0 1 0 Department of Epidemiology, Harvard T. H. Chan School of Public Health , 677 Huntington Avenue, Boston, MA 02115 , USA 1 Department of Epidemiology, Erasmus Medical Center , P.O. Box 2040, CA 3000, Rotterdam , The Netherlands When and why are causal diagrams useful? One of the most evident successes of causal diagrams is in supplementing story-telling. With a few arrows and letters, an investigator can tell a story of a data-generating process. Causal diagrams as (formal) story-telling - Communicating causality For a reader fluent in causal diagrams, even a dauntingly complex story can now be quickly and fully digested. In this way, we have seen a series of ‘‘paradoxes’’ demystified, including proposed explanations for the so-called Berkson’s [8], birth-weight [9], obesity [10], and Simpson’s [11] paradoxes. Similarly, causal diagrams focused our attention on the structures of oft overlooked potential biases, such as biases due to time-dependent confounding in stratification-based analyses [12], mediator-outcome confounding in mediation analyses [13], selecting on treatment in instrumental variable analyses [14], and na¨ıve per-protocol restrictions in randomized trial analyses [15]. Readers familiar with causal diagrams will recognize that many of these examples can be described as colliderstratification biases, and that, while some encompass previously recognized threats to validity, these potential biases were infrequently mentioned until their associated causal diagrams were drawn. Beyond demystifying perplexing patterns or illuminating subtle problems that exist across many studies, causal diagrams can also facilitate debates regarding a specific study’s conclusions. Consider two investigators who are in disagreement over whether a specific study’s analysis and conclusions were appropriate. If these two investigators ‘‘speak DAG’’ (directed acyclic graph) then they may seamlessly convey their assumptions and ideas to one another with little fear of miscommunication. Perhaps the two investigators will realize they had different causal diagrams in mind, and that favoring one analytic approach over another depends on which causal diagram is drawn— and thus on particular assumptions that, undrawn, might have suggested favoring a different analysis. Perhaps they will even be able to collect further data to help settle on which causal diagram—which set of assumptions—is more reasonable. Such discussions, which can be cumbersome and confusing without a formal language, can take place quickly and explicitly when supplemented with causal diagrams. In these ways, a causal diagram, like a picture, is worth one thousand words. Unlike artwork, however, where the ‘‘thousand words’’ convey a subjective perspective, a causal diagram should convey exactly the thousand words its creator and all other fluent readers would attribute to it. Causal diagrams are useful because they facilitate precise communication, but ignoring the formal rules that govern them can lead to miscommunication. For some examples of this, we can turn to an article in this issue of the European Journal of Epidemiology in which Greenland and Mansournia [3] caution how failing to read a causal DAG as encoding only structural (not random) confounding or failing to be explicit about faithfulness when presumed can lead readers of a causal diagram to perceive a different ‘‘thousand words’’ than intended. As with any tool that can streamline communication, there is also a danger of causal diagrams providing a false sense of security when they are constructed without investigators applying deep thought and subject matter knowledge. To see this, consider the use of causal diagrams in the context of instrumental variable analyses. Many epidemiology studies with instrumental variable analyses redraw the same textbook instrumental variable causal diagram to justify their analysis, yet the story is rarely as straightforward as the one depicted in that causal diagram. Herna´n and Robins [16], Swanson et al. [14] and VanderWeele et al. [17] have presented expanded versions of this standard graph that illustrate relatively subtle yet potentially common ways in which bias could arise. Thus, redrawing the textbook version of a causal diagram may oversimplify the likely data-generating process and even offer false comfort when applied to a specific study. Of note, some have argued that causal diagrams are not useful in the context of instrumental variable analyses because ‘‘the’’ DAG seems so simple that drawing it does not add to our understanding of the process [18]. While causal diagrams (arguably) add less to our understanding of what is a true instrument, we have seen many examples of causal diagrams adding substantially to our understanding of what is not an instrument. If two (...truncated)


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Sonja A. Swanson. Communicating causality, European Journal of Epidemiology, 2015, pp. 1073-1075, Volume 30, Issue 10, DOI: 10.1007/s10654-015-0086-6