Do Third-Party Web Traffic Estimates Preserve Causal Variation?

Researchers increasingly rely on third-party platforms such as Similarweb and Semrush to measure web traffic when first-party analytics are unavailable. Yet these platforms report model-generated estimates rather than raw data, raising questions about whether their measures preserve the temporal and cross-source variation required for causal inference. As a motivating diagnostic, we examine reported referral traffic around two documented search-engine outages; the absence of visible discontinuities illustrates why preservation of identifying variation cannot be taken for granted. We then characterize three mechanisms, within-source smoothing, cross-source leakage, and treatment-induced calibration error, through which platform processing can generate nonclassical outcome measurement error. Analytical results and a stylized difference-in-differences simulation show that this error can attenuate, amplify, or reverse estimated treatment effects. Our findings caution against using third-party traffic measures based on black-box proprietary models for causal inference.