[attribution] Methodological concerns regarding Attribution Level 1 and causal measurement

To the Attribution Working Group,

Thank you for the opportunity to comment on the Attribution Level 1
specification.

By way of background, I have worked in advertising measurement and
analytics for more than two decades, including research and analytics
leadership roles at DoubleClick and Google, and I currently focus on
incrementality measurement and experimental design.

At the outset, I want to acknowledge the legitimate and important goals
behind this work, particularly around privacy preservation, browser
interoperability, and reducing reliance on opaque third-party tracking
mechanisms. Standardized browser-level APIs for advertising-related event
reporting may offer meaningful improvements in transparency, consistency,
and user privacy relative to prior generations of ad-tech infrastructure.

Nothing in this comment should be interpreted as opposition to
privacy-preserving browser infrastructure itself.

My concern is instead with the underlying measurement paradigm being
standardized.

The specification appears to formalize and modernize a fundamentally
observational attribution framework that remains unable to reliably measure
advertising incrementality or causal advertising effectiveness.

Traditional attribution modeling generally does not estimate a
counterfactual. It observes advertising exposures and subsequent conversion
activity, then allocates credit across conversion pathways using
deterministic or statistical rules. This is not equivalent to measuring
whether advertising caused an incremental business outcome.

The core methodological problem is longstanding and well understood in both
academic and practitioner literature: consumers exposed to advertising are
not randomly selected. Users already close to purchase naturally generate
more searches, clicks, site visits, retail activity, and conversion events.
Attribution systems systematically confuse this underlying purchase
propensity with advertising persuasion.

This is not merely a weak form of experimental inference. In most
implementations, attribution modeling is not a causal methodology at all.

This concern is heightened by the specification’s repeated characterization
of attribution as a means of identifying “effective” advertising and
improving advertising performance. The document states that attribution
enables advertisers to learn “what ads perform best” and to spend more on
“effective advertising” and less on “ineffective advertising.” However,
observational attribution systems generally do not establish whether
advertising caused incremental outcomes. At best, they identify statistical
associations between exposures and subsequent actions.

Given the extensive longstanding critique of attribution modeling’s
inability to distinguish causation from selection effects and pre-existing
purchase intent, these claims risk overstating what such systems are
scientifically capable of determining.

The danger is not merely imprecision. It is the creation of a highly
persuasive but potentially misleading illusion of measurement validity,
particularly when supported by sophisticated dashboards, large-scale
behavioral telemetry, and algorithmic optimization systems.

These limitations have been extensively discussed for more than a decade by
researchers and practitioners, including within the attribution literature
itself.

Selected references include:


   - Dalessandro, Brian et al. “Causally Motivated Attribution for Online
   Advertising.” ADKDD 2012.
   https://www0.cs.ucl.ac.uk/staff/w.zhang/rtb-papers/causual-conv-att.pdf
   - Hercher, James. “Beware The Platform Incrementality Trap.”
   AdExchanger, 2025.
   https://www.adexchanger.com/commerce/beware-the-platform-incrementality-trap/

One particularly important consequence is structural lower-funnel bias.

Attribution systems systematically favor media channels positioned closest
to observable conversion activity, especially:

   - paid search,
   - retargeting,
   - retail media,
   - click-oriented social advertising,
   - and platforms with persistent identity and commerce visibility.

These systems are exceptionally effective at harvesting existing demand
signals. Modern advertising platforms increasingly use sophisticated AI and
algorithmic optimization systems to identify users exhibiting behavioral
indicators associated with imminent purchase intent. Because attribution
models observe users already in motion toward purchase, such channels tend
to receive disproportionate measurement credit regardless of their true
incremental contribution.

Conversely, media that often create demand rather than merely intercept it,
including television, audio, out-of-home, premium video, sponsorships, and
broader brand advertising, are structurally undercredited because their
effects are frequently delayed, probabilistic, indirect, or not easily tied
to clickstream behavior.

This bias does not merely distort reporting dashboards. It distorts budget
allocation, optimization systems, and ultimately competition within the
media marketplace.

The consequence is not merely technical mismeasurement. It is a systematic
reallocation of advertising investment toward the largest digital
platforms, particularly those with privileged access to high-intent
behavioral data, persistent identity systems, retail signals, and
large-scale AI optimization infrastructure. Standardizing
attribution-centric measurement frameworks risks further entrenching these
advantages at the expense of smaller media companies and independent
publishers whose contributions are less easily captured through clickstream
attribution. This concern is especially significant at a moment when many
media organizations, particularly news publishers, face severe economic
pressure and existential business challenges.

Modern advertising platforms increasingly optimize delivery algorithms
toward attributed conversions. This creates a self-reinforcing feedback
loop in which platforms learn to identify and target users already most
likely to convert, thereby improving attributed performance metrics without
necessarily improving incremental business outcomes.

The resulting feedback loop can create the illusion of advertising
effectiveness while increasingly concentrating spend into channels
optimized primarily for demand interception rather than incremental demand
creation. In this environment, advertisers themselves may become among the
largest losers, as substantial portions of marketing investment risk being
redirected toward measurement-favored but weakly incremental activity under
the implicit endorsement of major industry standards bodies and technical
organizations.

For this reason, I believe there is substantial risk in industry bodies and
standards organizations implicitly legitimizing attribution frameworks as
authoritative measures of advertising effectiveness without clearer
acknowledgment of their methodological limitations.

The concern is not that pathway telemetry lacks all utility. Descriptive
reporting of advertising exposures and conversion sequences can be
operationally useful for workflow management, reconciliation, and campaign
diagnostics. However, descriptive telemetry should not be conflated with
causal evidence of advertising effectiveness.

I would strongly encourage the Working Group to distinguish more clearly
between:

   1. privacy-preserving event reporting infrastructure, and
   2. scientifically valid methods for measuring advertising effectiveness.

The former is an important engineering and standards objective. The latter
remains fundamentally unresolved by attribution modeling itself.

The advertising industry — and especially influential standards and trade
organizations — should instead be advancing the science of advertising
measurement toward more scientifically grounded incrementality
methodologies, particularly randomized controlled experimentation and
large-scale geo-based experiments capable of estimating causal lift through
explicit counterfactual comparison.

For reference, I have published an overview of one such framework here:
https://github.com/rickcentralcontrolcom/geo-rct-methodology/

I would also encourage the specification to explicitly clarify that
Attribution Level 1 should not be interpreted as measuring causal
advertising effectiveness or incrementality absent separate experimental
methodology.

Absent such distinctions, there is a meaningful risk that the specification
will further institutionalize a measurement approach widely understood to
be methodologically inadequate for causal inference, with downstream
consequences for advertiser decision quality, media competition, and the
allocation of advertising investment across the digital ecosystem.

Rick Bruner
CEO, Central Control
Founder & Moderator, Research Wonks
U.S. Vice Chair, I-COM