The inference from converging experimental findings to a real causal relationship fails because the largest and most direct experiments (2023 Meta studies) found null effects, contradicting rather than supporting triangulation.
The 2023 Meta experiments directly testing algorithmic versus chronological feeds found no detectable polarization effects.
Triangulation requires convergence across studies; when the most methodologically rigorous studies find null effects, earlier smaller studies do not triangulate to a positive conclusion.
Is there a plausible alternative hypothesis that has not been considered or that would explain the facts at least as well?
Could the facts be jointly explained by a conjunction of weaker causes rather than a single dominant H?
Could the body of facts F itself be an artifact of selection, measurement, or reporting bias rather than a real phenomenon needing causal explanation?
Does H actually explain the full body of facts F, or only a salient subset?
Are the criteria used to judge H 'best' (scope, simplicity, mechanism, prior probability) appropriate for this domain, and are they applied consistently across the alternatives?