Use Causal Language: Ignore the Haters

Correlation is not causation. So comes the inevitable refrain in response to anyone who presents a correlational study as evidence in a debate. There is good reason for this. People have long extrapolated from correlation to causation. Bad science and often bad policy follows. But a healthy respect for what can what we can claim about causality has given way to abject fear of any language that even hints at causality. 

There is no danger in being overly cautious I hear you say. 

But there have been unintentional consequences that have resulted from the barring of causal language. First, few social scientists now understand much about causality, mistakenly thinking it is that which comes from an RCT. Second, theory has become sloppy. Why waste time constructing a detailed theory of why x leads to y when a reviewer will make you tear it up.

Evidence that something has gone wrong

The biggest evidence I see that something is amiss is how reviewers and writers now interact. It is not uncommon to have a reviewer demand a writer remove all causal language from their manuscript. I have seen this include purging the word ‘effect’ from a manuscript entirely; even named theories are not immune (the Big-Fish-Little-Pond effect becomes the Big-Fish -Little-Pond association). But authors should advance causal theories in introductions!

Reviewers also display a lack of understanding about causation when they claim only an RCT can provide evidence of causality. RCTs neither provide definitive evidence of causation nor are they only way of providing evidence of causality.  

Writers also make mistakes. Writers of papers I have reviewed refuse to explain how x leads to y because they didn’t do an RCT. One wonders, if they think this way, why they bothered to do the study at all. And if they are so scared of advancing a causal explanation why use a regression model that so strongly suggests that x leads to y?

Setting the record straight

 In Hunting causes and using them Nancy Cartwright emphasizes causality is not a single thing (it is also worth reading her book Evidence Based Policy). So heterogeneous are the things we call causality that we might be better to abandon the term entirely. We likely need to match method and evidence to the type of causality we are chasing. 

Judea Pearl in The book of why claims science would become worse not better if we were to believe that only RCTs have the power to provide evidence of causation. In such a world how would we know that smoking causes cancer? 

The danger in the current social science landscape comes from a belief that causation is a dichotomy. If you did an RCT, you can advance causal claims. If you didn’t, you can’t. But causality is not a dichotomy. RCTs often can’t provide evidence of causation and sometimes provide poor evidence. RCTs are critical but we both need to be more conservative—RCTs provide some evidence sometimes—and be more liberal in allowing other designs (regression of discontinuity, instrumental variables, Granger causality) to provide evidence of causality.

What to do about it

  1. Treat causality as a spectrum where researchers can marshal evidence that push the needle toward a causal interpretation or away from it.
  2. View no single piece of evidence an inconvertible evidence of causality.
  3. Write about clear and simple causal mechanisms in introductions and literature reviews.
  4. In the method section, social science should have a section on the degree to which the results can provide evidence of causation; perhaps also the type of causation they have in mind. This should include a discussion of design, context, strength of theory, and methodology. In other words, researchers should have to make a case for their specific research rather than relying on general social science tropes.
  5. As Cartwright suggests, we should replace general terms like cause with more specific terms like repel, pull, excite, or suppress that give a better idea of what is being claimed.

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