Do Third-Party Web Traffic Estimates Preserve Causal Variation?

2026-09-24

AI Summary

This study investigates whether third-party traffic estimation data satisfy the requirements for causal inference. Focusing on non-classical measurement error induced by analytics platform processing, it systematically identifies three mechanisms—smoothing, leakage, and calibration errors—through analytical derivations and difference-in-differences simulation experiments. The findings demonstrate that such errors can attenuate, amplify, or even reverse estimated causal effects. By revealing the latent risks of employing black-box data for causal inference, this work provides a theoretical foundation and methodological caution for the judicious use of third-party traffic data in empirical research.

Abstract

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.

Problem

• third-party web traffic estimates
• causal inference
• measurement error
• identifying variation
• black-box models

Innovation

• causal inference
• measurement error
• third-party web traffic
• difference-in-differences
• black-box models