# Causal Formalism and RCTs

**URL:** <https://discourse.datamethods.org/t/causal-formalism-and-rcts/28854>\
**Category:** causal inference\
**Tags:** design, interpretation\
**Created:** [September 10, 2026, 2:33pm UTC](https://discourse.datamethods.org/t/causal-formalism-and-rcts/28854 "2026-09-10T14:33:46Z")\
**Posts on this page:** 1\
**Showing post:** 26

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**Author:** ![llynn](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/llynn/32/2827_2.png) [@llynn](https://discourse.datamethods.org/u/llynn)\
**Post date:** [September 30, 2026, 2:42am UTC](https://discourse.datamethods.org/t/causal-formalism-and-rcts/28854/26 "2026-09-30T02:42:10Z")

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> [@f2harrell](#):
>
> this need not stay with the “safe stuff”

Very well.

Recognition that cause-agnostic RCTs (CARs) can be an unsafe standard methodology makes causal formalism necessary. But why are they unsafe? They are based on poor priors. If the eligibility rule is only weakly coupled to the mechanism the treatment is supposed to act on, the prior probability that a positive result is a _true disease-level effect_ is lower.

Causal formalism distinguishes who enters an RCT from the disease process the treatment actually affects. Complete mechanistic knowledge is not required, but the entry criteria must be linked, at least probabilistically, to the process being treated. This is consistent with a Bayesian strengthening of RCT design: when the treatment and eligibility gate are poorly matched, the probability of a true treatment effect is lower, increasing the probability that a positive result is false. It also increases the risk of “false transport” (applying a false positive RCT result to clinical Guidelines.

The “reproducibility crisis” is a misnomer. The proper term is a “false transportability” crisis. This is cause by standardized cause agnostic gates which render insufficient priors .Bayesian approaches which do not interrogate the gate (like the failed 2025 RE MAP CAP) are “Bayesian facades”.

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