The growing interest in integrating causal inference and Design Theory

Elias I wanted to get back to discuss this with you. I hope you will provide your thoughts.

The issue raised by the proposed cSM framework is that current trial design imposes no structural requirements on the selection rule S (nor, more broadly, on the covariate set). Indeedthere is no requirement for S to correspond to a biologic entity, disease, or mechanistic target. Instead, it may be defined by a synthetic data-generating process (SDGP) , that is, a constructed gate based on thresholds or consensus criteria, much like the original formulation of SIRS. The potential for SDGP gated RCT is therefore unlimited but the output does not generate buildable knowledge as is the case with 34 years of sepsis RCT and the tragedy of false RCT transport of theARDS meta analysis to guidelines for early ventilator treatment of severe COVID pneumonia.

When S is synthetic in this way, it can aggregate distinct causal systems under a single enrollment criterion. The resulting trial estimand,

E [Y1 - Y0 |S = 1 ],

remains internally well-defined, but its meaning is conditioned on a gate that does not correspond to a coherent biologic data-generating process.

This gives rise to a third-layer estimand : one that is mathematically precise yet dependent on the structure of S rather than anchored to a stable causal target. As a consequence, transportability may be systematically unsafe because the estimand reflects the composition induced by the selection rule , not a reproducible underlying mechanism.

The prevention of this pathological third layer is the responsibility of the statistician as they understand RCT structure and the clinical trialist may not. This is why cSM or other means for the statisticians to formally dissect S and define the rules for an acceptable Sis required.