The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data

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Abstract

Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.

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The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.

Sander Greenland, Daniel Westreich (2013)

It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.

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Does water kill? A call for less casual causal inferences.

Miguel A. Hernán (2016)

"Can this number be interpreted as a causal effect?" is a key question for scientists and decision makers. The potential outcomes approach, a quantitative counterfactual theory, describes conditions under which the question can be answered affirmatively. This article reviews one of those conditions, known as consistency, and its implications for real world decisions.