EDITORIAL
Why You Want to Avoid Being a Causist Robin D. Froman, Steven V. Owen
Correspondence to Robin D. Froman, E-mail: [email protected] Robin D. Froman Associate Editor, Research in Nursing & Health 2420 Toyon Dr. Healdsburg, CA 95448
Keywords: causation; causality; causism; association; correlation; research design; writing for publication Research in Nursing & Health, 2014, 37, 171–173 Accepted 12 March 2014 DOI: 10.1002/nur.21597 Published online 9 April 2014 in Wiley Online Library (wileyonlinelibrary.com).
Steven V. Owen University Professor Emeritus, University of Connecticut
One of the most basic principles and well-known assertions in quantitative research is that correlation does not demonstrate causation. The intent of this editorial is to review why correlation does not equal cause, to identify places in research reports where language and planning might mistakenly confuse correlation and cause, to provide an overview of the uses and limits of statistical methods that aim to demonstrate cause, and to speculate about why the problem persists. Along the way, we hope to show that conflating correlation with causation can interfere with research and knowledge development, and to argue emphatically that researchers and scientists should avoid “causism.”
Why Correlation Does Not Equal Cause The problem of equating correlation with causation is not new. The Romans used the expression “cum hoc ergo propter hoc” for the faulty assumption that if two events cooccurred, causation was likely. The Latin translates to “with this, therefore because of this,” a longstanding logical fallacy. In the modern psychosocial research literature, Rosenthal (1994) named the phenomenon causism and defined it as “the tendency to imply a causal relationship where none has been established (i.e., where the data do not support it)” (p. 127). In his essay, he pointed out that causism may merely reflect poor scientific training, or it may be a misrepresentation of data that is potentially unethical, as will be discussed further below. If correlations between variables are insufficient to document causation, what is needed to show a connection between cause and consequence? Most agree that active
manipulation of at least one variable and inspection of outcomes after that manipulation is the best way to show causation. This is the basis for the high regard in science for experimental studies, preferably those using randomized controlled trials (RCTs). But even if RCTs are the gold standard, they cannot deliver all the information we want. For example, even the best RCT can demonstrate only an average effect for an entire group. It cannot show individual variations, that is, cases for whom the experimental manipulation works especially well or especially badly (Rubin, 2005). As examples of correlation not equaling causation, consider the relationship between nursing salaries and quality indicators for hospital performance. The two show a significant, modest, and positive relationship. So, does that suggest that salaries cause indicators to improve, or that indicator improvement leads to raises in nurses' salaries? Actually, neither of the causal conclusions is true. A third variable, possibly a much more complicated and multifaceted factor, such as hospital infrastructure, probably affects both outcomes. It would be foolish to propose raising salaries to improve indicators, or using improved indicator outcomes as the criterion for general decisions on salary. Some might argue that if a temporal relationship is present between variables, with one preceding the other, the causal assumption is strengthened. Consider the relationship between monitoring fluid intake and incidence of urinary retention. Clearly, fluid intake precedes urine output, but does documenting the volume of fluids into the system cause the urinary elimination or retention? The recording itself is unlikely to cause any patient response directly, but it might lead to other variables that have causative
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influence, such as nurses' urging of patients to urinate, or catheterizing in the case of no urine output. Even though the documentation preceded urination, it does not cause it. Once again a third unmeasured variable, other associated nurses' actions, is more likely the causal agent.
Where Language and Planning Might Confuse Correlation and Cause Causism raises its head in three distinct places in research reports: the review of the literature, methods planning, and conveying results and implications in the conclusion and discussion. When describing what has been reported by others in the literature, there are certain “loaded” words that should be avoided if studies reviewed are correlational. Use of terms such as impact, influence, effect, the consequence of, the result of, and determined by all imply cause. Without an actual experimental design, in which conditions are randomly assigned and variables have been actively manipulated, such language is inappropriate. More conservative and appropriate language in reviews of correlational studies includes “was related to” (or associated with), “was predictable from,” or “could be inferred from.” When presenting the review of the literature, author's claims should be interpreted with caution. A title such as “Dietary disruption and its effects on health of family caregivers” would be an overstatement of findings, if a correlational study had been conducted. The ethical considerations of disrupting caregivers' diets would likely preclude a researcher's manipulation of diet, and the correlational design allows only identification of associations, not effects. When planning studies and devising designs and procedures, some active manipulation of variables expected to exert influence is required to unequivocally ascribe cause. Methods and design experts agree that the strongest way to document causation is through use of experimental designs, preferably ones that employ random assignment. The classic research design reference by Shadish, Cook, and Campbell (2002), and its predecessor by Cook and Campbell (1979), distinguished between quasi-experimental and experimental designs. Shadish et al. and Rosenthal (1994) asserted that quasi-experimental designs cannot easily demonstrate causation and should not be presented to institutional review boards (IRBs) as a means to show causation. Rosenthal went so far as to assert that “as a member of an IRB I regret that I have seen such use made of causist language in proposals brought to us” (p. 129). An inappropriate assertion of causation in an IRB application might mistakenly lead a board to consider that active intervention is in order, when preliminary data showing solid evidence of cause are still absent. Rosenthal (1994) explained how causism interferes with the logic and ethics of research. Unfortunately, although determining causation might be a researcher's goal, random assignment and manipulation of many
Research in Nursing & Health
psychosocial variables pose ethical concerns. We cannot ethically, or in some cases feasibly, manipulate such things as dietary disruption, culture, economic situation, sleep, disease state, or gender. These variables are typically tied to outcomes through correlational studies, thus limiting the ability to ascribe causal influence. Such variables can be identified as predictors based upon statistical relationships, or said to show associations with outcomes, but cannot confidently be identified as causes of outcomes if they have not been actively manipulated or special statistical approaches used (see below). Researchers planning and executing studies with the aim to identify causation need to acknowledge this. Studying correlated variables is a preliminary step in the direction of identifying cause and possible venues for interventions, but associations do not show influence. In general, results from correlational data create—but do not answer—causal hypotheses. The interpretation and discussion of findings is the third place where causism can creep in to muddle our thinking. Correlation without manipulation shows the ability to predict one variable from knowledge of another, and shows associations, but does not show interventional influence. Thus, findings from bivariate and even multivariate approaches such as regression analysis are still limited in the conclusions that can be drawn. It would be premature for a researcher who has studied the multivariate relationship of cultural affiliation, self-efficacy, and age as predictors of caregiver burden to conclude that changing any of the predictors would alter the outcome of burden. Intervention studies are needed to come responsibly to such conclusions.
Statistical Methods That Aim toward Demonstrating Cause In the last 40 years, there has been an explosion of interest in structural equation modeling (SEM), sometimes unfortunately called “causal modeling.” Certainly, SEM can deliver causal conclusions when it is used with experimental data. But SEM is overwhelmingly used with correlational data, and the strongest possible conclusion in that case is to reject a causal claim. That is, SEM is a good tool for ruling out hypothesized causal pathways. On the other hand, more recent work offers limited and challenging opportunities for drawing causal conclusions from correlational data. We say limited because the approach requires attention to an extensive list of problems. Antonakis, Bendahan, Jacquart, and Lalive (2010, p. 1086) summarized the list: “[E]ndogeneity – which includes omitted variables, omitted selection, simultaneity, commonmethod variance, and measurement error – renders estimates causally uninterpretable.” Antonakis et al. then presented five non-experimental designs that may help in drawing tentative causal inference. While cautioning that, in a search for causal effects, quasi-experimental designs are
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a collective second choice to RCTs, Antonakis et al. (2010) suggested that where ethical or practical issues make the RCT impossible, replicated second choices can deliver tentative causal conclusions. If statistical assumptions are met, those second choices can approach what Platt (1964) called “strong inference.” In addition, Antonakis et al. (2010) suggest we should exploit more extensive and robust statistical packages, such as R, SYSTAT, or MPlus. However, even the best statistical packages do not (yet) have the capacity to support Pearl's (2009) recommended approaches to causal inference from non-experimental data. Pearl (2014) has regularly chided software developers for being slow to adopt statistical routines for his many discoveries (e.g., mutilated diagram, skipping collision nodes, d-separation, directed acyclic graphs, collider bias, reverse regression). If you are up to the challenge of learning advanced statistical methods and new vocabulary, Elwert (2013) offered a chapter-length primer on the latest methods.
quasi-experimental methods. It is generally weak inference, though, to bypass the intervention study and to promote interventions directly from a correlational study. In an essay in the New York Times, Taubes (2014) presented an interesting and provocative discussion on why, after 50 years of study and 600,000 articles on obesity and diabetes, both health problems continue to increase. His simple description of the protocol of science is to hypothesize and then test. But in the case of humans' diets, this cannot be done easily; thus, in the absence of controlled experimental studies even in the straightforward area of weight management, we are left with speculations and associations. If we cannot avoid this pickle, he suggested that we acknowledge the limits of associations and not ignore them. To do so is an honest presentation, neither inflating data nor ignoring what has been accomplished so far. So, if others' or your data are limited to correlations, be conservative and do not claim causal influence. Your evidence of associations and correlations can lead to the next steps, when possible, of actually showing cause.
Why Causism Persists We should point out that nurse researchers are not alone in causal misinterpretation. Antonakis et al. (2010) pointed fingers at social science research in general. Their review of 110 articles in leadership research showed wrong causal claims in as many as 90% of the studies. In short, correlational data should not easily invite causal conclusions. Then why is the practice so widespread? There are four reasons. First, many researchers never learned or have forgotten the statistical bromide that correlation does not mean causation. Researchers observe many others who successfully publish causal exaggerations. The second reason is a subset of the first. Journal editors and peer reviewers are the gatekeepers of published research. They bear some of the responsibility for approving and printing inappropriate causal claims, especially because their research expertise has put them in a leadership role. Sometimes the gates are not so well-kept. The third reason is that many researchers, strapped for time and resources, skip original articles and rely on secondary sources. Secondary sources—including popular media—can magnify contaminated literature by combining doubtful conclusions and creating the illusion of broad agreement and causation. The fourth reason involves a longstanding culture within biomedical and health research, particularly nursing research. For decades, there has been an expectation that a Discussion section should contain implications for practice. Certainly, results of a correlational study can inspire hypotheses about how specific interventions might bring about certain outcomes. Those hypotheses in turn invite carefully designed intervention studies, or at least strong
Research in Nursing & Health
References Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120. doi: http://dx.doi.org/ 10.1016/j.leaqua.2010.10.010 Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally. Elwert, F. (2013). Graphical causal models. In: S. Morgan (Ed.), Handbook of causal analysis for social research (pp. 245–273). New York: Sage Publications. Also available at: http://www.ssc. wisc.edu/soc/faculty/pages/docs/elwert/Elwert%202013.pdf (retrieved 21 February 2014). Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, England: Cambridge University Press. Pearl, J. (2014). Postings on SEMNET (Structural Equation Modeling NETwork). Available at: https://listserv.ua.edu/archives/semnet. html Platt, J. R. (1964). Strong inference. Science, 146, 347–353. Rosenthal, R. (1994). Science and ethics in conducting, analyzing, and reporting psychological research. Psychological Science, 5, 127–134. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331. doi: 10.1198/016214504000001880 Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton-Mifflin. Taubes, G. (2014). http://www.nytimes.com/2014/02/09/opinion/sunday/ why-nutrition-is-so-confusing.html. Retrieved 21 February 2014.
Why You Want to Avoid Being a Causist Robin D. Froman, Steven V. Owen
Correspondence to Robin D. Froman, E-mail: [email protected] Robin D. Froman Associate Editor, Research in Nursing & Health 2420 Toyon Dr. Healdsburg, CA 95448
Keywords: causation; causality; causism; association; correlation; research design; writing for publication Research in Nursing & Health, 2014, 37, 171–173 Accepted 12 March 2014 DOI: 10.1002/nur.21597 Published online 9 April 2014 in Wiley Online Library (wileyonlinelibrary.com).
Steven V. Owen University Professor Emeritus, University of Connecticut
One of the most basic principles and well-known assertions in quantitative research is that correlation does not demonstrate causation. The intent of this editorial is to review why correlation does not equal cause, to identify places in research reports where language and planning might mistakenly confuse correlation and cause, to provide an overview of the uses and limits of statistical methods that aim to demonstrate cause, and to speculate about why the problem persists. Along the way, we hope to show that conflating correlation with causation can interfere with research and knowledge development, and to argue emphatically that researchers and scientists should avoid “causism.”
Why Correlation Does Not Equal Cause The problem of equating correlation with causation is not new. The Romans used the expression “cum hoc ergo propter hoc” for the faulty assumption that if two events cooccurred, causation was likely. The Latin translates to “with this, therefore because of this,” a longstanding logical fallacy. In the modern psychosocial research literature, Rosenthal (1994) named the phenomenon causism and defined it as “the tendency to imply a causal relationship where none has been established (i.e., where the data do not support it)” (p. 127). In his essay, he pointed out that causism may merely reflect poor scientific training, or it may be a misrepresentation of data that is potentially unethical, as will be discussed further below. If correlations between variables are insufficient to document causation, what is needed to show a connection between cause and consequence? Most agree that active
manipulation of at least one variable and inspection of outcomes after that manipulation is the best way to show causation. This is the basis for the high regard in science for experimental studies, preferably those using randomized controlled trials (RCTs). But even if RCTs are the gold standard, they cannot deliver all the information we want. For example, even the best RCT can demonstrate only an average effect for an entire group. It cannot show individual variations, that is, cases for whom the experimental manipulation works especially well or especially badly (Rubin, 2005). As examples of correlation not equaling causation, consider the relationship between nursing salaries and quality indicators for hospital performance. The two show a significant, modest, and positive relationship. So, does that suggest that salaries cause indicators to improve, or that indicator improvement leads to raises in nurses' salaries? Actually, neither of the causal conclusions is true. A third variable, possibly a much more complicated and multifaceted factor, such as hospital infrastructure, probably affects both outcomes. It would be foolish to propose raising salaries to improve indicators, or using improved indicator outcomes as the criterion for general decisions on salary. Some might argue that if a temporal relationship is present between variables, with one preceding the other, the causal assumption is strengthened. Consider the relationship between monitoring fluid intake and incidence of urinary retention. Clearly, fluid intake precedes urine output, but does documenting the volume of fluids into the system cause the urinary elimination or retention? The recording itself is unlikely to cause any patient response directly, but it might lead to other variables that have causative
C
2014 Wiley Periodicals, Inc.
172
RESEARCH IN NURSING & HEALTH
influence, such as nurses' urging of patients to urinate, or catheterizing in the case of no urine output. Even though the documentation preceded urination, it does not cause it. Once again a third unmeasured variable, other associated nurses' actions, is more likely the causal agent.
Where Language and Planning Might Confuse Correlation and Cause Causism raises its head in three distinct places in research reports: the review of the literature, methods planning, and conveying results and implications in the conclusion and discussion. When describing what has been reported by others in the literature, there are certain “loaded” words that should be avoided if studies reviewed are correlational. Use of terms such as impact, influence, effect, the consequence of, the result of, and determined by all imply cause. Without an actual experimental design, in which conditions are randomly assigned and variables have been actively manipulated, such language is inappropriate. More conservative and appropriate language in reviews of correlational studies includes “was related to” (or associated with), “was predictable from,” or “could be inferred from.” When presenting the review of the literature, author's claims should be interpreted with caution. A title such as “Dietary disruption and its effects on health of family caregivers” would be an overstatement of findings, if a correlational study had been conducted. The ethical considerations of disrupting caregivers' diets would likely preclude a researcher's manipulation of diet, and the correlational design allows only identification of associations, not effects. When planning studies and devising designs and procedures, some active manipulation of variables expected to exert influence is required to unequivocally ascribe cause. Methods and design experts agree that the strongest way to document causation is through use of experimental designs, preferably ones that employ random assignment. The classic research design reference by Shadish, Cook, and Campbell (2002), and its predecessor by Cook and Campbell (1979), distinguished between quasi-experimental and experimental designs. Shadish et al. and Rosenthal (1994) asserted that quasi-experimental designs cannot easily demonstrate causation and should not be presented to institutional review boards (IRBs) as a means to show causation. Rosenthal went so far as to assert that “as a member of an IRB I regret that I have seen such use made of causist language in proposals brought to us” (p. 129). An inappropriate assertion of causation in an IRB application might mistakenly lead a board to consider that active intervention is in order, when preliminary data showing solid evidence of cause are still absent. Rosenthal (1994) explained how causism interferes with the logic and ethics of research. Unfortunately, although determining causation might be a researcher's goal, random assignment and manipulation of many
Research in Nursing & Health
psychosocial variables pose ethical concerns. We cannot ethically, or in some cases feasibly, manipulate such things as dietary disruption, culture, economic situation, sleep, disease state, or gender. These variables are typically tied to outcomes through correlational studies, thus limiting the ability to ascribe causal influence. Such variables can be identified as predictors based upon statistical relationships, or said to show associations with outcomes, but cannot confidently be identified as causes of outcomes if they have not been actively manipulated or special statistical approaches used (see below). Researchers planning and executing studies with the aim to identify causation need to acknowledge this. Studying correlated variables is a preliminary step in the direction of identifying cause and possible venues for interventions, but associations do not show influence. In general, results from correlational data create—but do not answer—causal hypotheses. The interpretation and discussion of findings is the third place where causism can creep in to muddle our thinking. Correlation without manipulation shows the ability to predict one variable from knowledge of another, and shows associations, but does not show interventional influence. Thus, findings from bivariate and even multivariate approaches such as regression analysis are still limited in the conclusions that can be drawn. It would be premature for a researcher who has studied the multivariate relationship of cultural affiliation, self-efficacy, and age as predictors of caregiver burden to conclude that changing any of the predictors would alter the outcome of burden. Intervention studies are needed to come responsibly to such conclusions.
Statistical Methods That Aim toward Demonstrating Cause In the last 40 years, there has been an explosion of interest in structural equation modeling (SEM), sometimes unfortunately called “causal modeling.” Certainly, SEM can deliver causal conclusions when it is used with experimental data. But SEM is overwhelmingly used with correlational data, and the strongest possible conclusion in that case is to reject a causal claim. That is, SEM is a good tool for ruling out hypothesized causal pathways. On the other hand, more recent work offers limited and challenging opportunities for drawing causal conclusions from correlational data. We say limited because the approach requires attention to an extensive list of problems. Antonakis, Bendahan, Jacquart, and Lalive (2010, p. 1086) summarized the list: “[E]ndogeneity – which includes omitted variables, omitted selection, simultaneity, commonmethod variance, and measurement error – renders estimates causally uninterpretable.” Antonakis et al. then presented five non-experimental designs that may help in drawing tentative causal inference. While cautioning that, in a search for causal effects, quasi-experimental designs are
173
AVOIDING CAUSISM/ FROMAN AND OWEN
a collective second choice to RCTs, Antonakis et al. (2010) suggested that where ethical or practical issues make the RCT impossible, replicated second choices can deliver tentative causal conclusions. If statistical assumptions are met, those second choices can approach what Platt (1964) called “strong inference.” In addition, Antonakis et al. (2010) suggest we should exploit more extensive and robust statistical packages, such as R, SYSTAT, or MPlus. However, even the best statistical packages do not (yet) have the capacity to support Pearl's (2009) recommended approaches to causal inference from non-experimental data. Pearl (2014) has regularly chided software developers for being slow to adopt statistical routines for his many discoveries (e.g., mutilated diagram, skipping collision nodes, d-separation, directed acyclic graphs, collider bias, reverse regression). If you are up to the challenge of learning advanced statistical methods and new vocabulary, Elwert (2013) offered a chapter-length primer on the latest methods.
quasi-experimental methods. It is generally weak inference, though, to bypass the intervention study and to promote interventions directly from a correlational study. In an essay in the New York Times, Taubes (2014) presented an interesting and provocative discussion on why, after 50 years of study and 600,000 articles on obesity and diabetes, both health problems continue to increase. His simple description of the protocol of science is to hypothesize and then test. But in the case of humans' diets, this cannot be done easily; thus, in the absence of controlled experimental studies even in the straightforward area of weight management, we are left with speculations and associations. If we cannot avoid this pickle, he suggested that we acknowledge the limits of associations and not ignore them. To do so is an honest presentation, neither inflating data nor ignoring what has been accomplished so far. So, if others' or your data are limited to correlations, be conservative and do not claim causal influence. Your evidence of associations and correlations can lead to the next steps, when possible, of actually showing cause.
Why Causism Persists We should point out that nurse researchers are not alone in causal misinterpretation. Antonakis et al. (2010) pointed fingers at social science research in general. Their review of 110 articles in leadership research showed wrong causal claims in as many as 90% of the studies. In short, correlational data should not easily invite causal conclusions. Then why is the practice so widespread? There are four reasons. First, many researchers never learned or have forgotten the statistical bromide that correlation does not mean causation. Researchers observe many others who successfully publish causal exaggerations. The second reason is a subset of the first. Journal editors and peer reviewers are the gatekeepers of published research. They bear some of the responsibility for approving and printing inappropriate causal claims, especially because their research expertise has put them in a leadership role. Sometimes the gates are not so well-kept. The third reason is that many researchers, strapped for time and resources, skip original articles and rely on secondary sources. Secondary sources—including popular media—can magnify contaminated literature by combining doubtful conclusions and creating the illusion of broad agreement and causation. The fourth reason involves a longstanding culture within biomedical and health research, particularly nursing research. For decades, there has been an expectation that a Discussion section should contain implications for practice. Certainly, results of a correlational study can inspire hypotheses about how specific interventions might bring about certain outcomes. Those hypotheses in turn invite carefully designed intervention studies, or at least strong
Research in Nursing & Health
References Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120. doi: http://dx.doi.org/ 10.1016/j.leaqua.2010.10.010 Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally. Elwert, F. (2013). Graphical causal models. In: S. Morgan (Ed.), Handbook of causal analysis for social research (pp. 245–273). New York: Sage Publications. Also available at: http://www.ssc. wisc.edu/soc/faculty/pages/docs/elwert/Elwert%202013.pdf (retrieved 21 February 2014). Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, England: Cambridge University Press. Pearl, J. (2014). Postings on SEMNET (Structural Equation Modeling NETwork). Available at: https://listserv.ua.edu/archives/semnet. html Platt, J. R. (1964). Strong inference. Science, 146, 347–353. Rosenthal, R. (1994). Science and ethics in conducting, analyzing, and reporting psychological research. Psychological Science, 5, 127–134. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331. doi: 10.1198/016214504000001880 Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton-Mifflin. Taubes, G. (2014). http://www.nytimes.com/2014/02/09/opinion/sunday/ why-nutrition-is-so-confusing.html. Retrieved 21 February 2014.
