Science — the need for causal explanation

22 Sep, 2022 at 16:29 | Posted in Theory of Science & Methodology | Leave a comment

Many journal editors request authors to avoid causal language, and many observational researchers, trained in a scientific environment that frowns upon causality claims, spontaneously refrain from mentioning the C-word (“causal”) in their work …

Causal Inference that's not A/B Testing: Theory & Practical Guide | by Eva  Gong | Towards Data ScienceThe proscription against the C-word is harmful to science because causal inference is a core task of science, regardless of whether the study is randomized or nonrandomized. Without being able to make explicit references to causal effects, the goals of many observational studies can only be expressed in a roundabout way. The resulting ambiguity impedes a frank discussion about methodology because the methods used to estimate causal effects are not the same as those used to estimate associations. Confusion then ensues at the most basic levels of the scientific process and, inevitably, errors are made …

We all agree: confounding is always a possibility and therefore association is not necessarily causation. One possible reaction is to completely ditch causal language in observational studies. This reaction, however, does not solve the tension between causation and association; it just sweeps it under the rug …

Without causally explicit language, the means and ends of much of observational research get hopelessly conflated … Carefully distinguishing between causal aims and associational methods is not just a matter of enhancing scientific communication and transparency. Eliminating the causal–associational ambiguity has practical implications for the quality of observational research too.

Miguel Hernán

Highly recommended reading!

Causality in social sciences — and economics — can never solely be a question of statistical inference. Causality entails more than predictability, and to really explain social phenomena require theory. Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. First, when we are able to tie actions, processes or structures to the statistical relations detected, can we say that we are getting at relevant explanations of causation.

Most facts have many different, possible, alternative explanations. Still, we want to find the best of all contrastive (since all real explanation takes place relative to a set of alternatives) explanations. So which is the best explanation? Many scientists, influenced by statistical reasoning, think the likeliest explanation is the best. But the likelihood of x is not in itself a strong argument for thinking it explains y. I would rather argue that what makes one explanation better than another are things like aiming for and finding powerful, deep, causal, features and mechanisms that we have warranted and justified reasons to believe in. Statistical — especially the variety based on a Bayesian epistemology — reasoning generally has no room for these explanatory considerations. The only thing that matters is the probabilistic relation between evidence and hypothesis. That is also one of the main reasons I find abduction — inference to the best explanation — a better description and account of what constitutes actual scientific reasoning and inferences.

Some statisticians and data scientists think that algorithmic formalisms somehow give them access to causality. That is, however, simply not true. Assuming ‘convenient’ things like faithfulness or stability is not to give proof. It’s to assume what has to be proven. Deductive-axiomatic methods used in statistics do not produce evidence for causal inferences. The real causality we are searching for is the one existing in the real world around us. If there is no warranted connection between axiomatically derived theorems and the real world, well, then we haven’t really obtained the causation we are looking for.

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