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

Thanks Chris - your point extends the case. If H3 is in or derived from the
bid request, the same canonical bucket serves both bid-time reach/frequency
management and post-bid attribution histograms.

Two requirements this puts on browser attribution:

   1. Browser attribution needs to be extensible so it fits into external
   measurement frameworks (geo RCTs, MMM, panel).
   2. The attribution model's parameters need to be tunable so outputs are
   cross-validatable against external measurement.

Standardized H3 × ISO-week × hour parameters align with both - it is the
same dimension and on both sides of the transaction.

Best, Nate



On Mon, Jun 1, 2026 at 9:49 PM Chris Williams <chris_williams65@icloud.com>
wrote:

> Geo-temporal also takes care of reach and frequency if the Hex is included
> in the bid request.
> The buying agent makes decisions on how many impressions (and which format
> the impressions are in) are required in each Hex to achieve Reach goals and
> manage frequency DURING the buy not measuring it after.
>
>
> [image: 1777905482258.png]
>
> Beyond Programmatic: A Holistic Architecture for Agentic Media
> <https://www.linkedin.com/pulse/beyond-programmatic-holistic-architecture-agentic-media-williams-3eb9f/>
> linkedin.com
> <https://www.linkedin.com/pulse/beyond-programmatic-holistic-architecture-agentic-media-williams-3eb9f/>
>
> <https://www.linkedin.com/pulse/beyond-programmatic-holistic-architecture-agentic-media-williams-3eb9f/>
>
>
> On Jun 1, 2026, at 5:39 PM, Nathan Woodman <nate@proofindata.com> wrote:
>
> Hi Rick, Charlie, all,
>
> I want to support Rick's argument for geo-based methods and engage with
> Charlie's acknowledgment that cross-site diversion is the unsolved gap, by
> suggesting a specific path forward: standardize the unit of analysis.
> Once the unit is standardized, the inputs and the experimental designs that
> consume it can remain open.
>
> The structural insight from Rick's geo-RCT methodology (
> https://github.com/rickcentralcontrolcom/geo-rct-methodology) is that
> geography sidesteps the cross-site diversion problem entirely. Every
> operator - every publisher, every advertiser, every measurement vendor,
> every channel including CTV, out-of-home, audio, and in-store outside the
> browser, sees the same geographic units. No identity bridging required. The
> randomization unit is consistent across the ecosystem by construction.
>
> But geography alone isn't enough. The unit of analysis is spatial ×
> temporal, and both need to be standardized for cross-channel composition
> to actually work. A CTV ad airing Wednesday 8pm Eastern, an OOH impression
> at a billboard at 8:15pm, and a web conversion at 8:30pm only become
> composable across channels if they all resolve to the same canonical (cell,
> time-bin) buckets.
>
> Specifically I'd recommend:
>
> Spatial unit - Uber H3 hexagonal hierarchical grid, with R8 (~460m hex
> edge) as the default resolution. H3 has properties well-suited to
> advertising measurement:
>
>
>    - Hexagonal cells are uniform in neighbor relationships (no
>    diagonal-vs-orthogonal asymmetry that grids have).
>    - Hierarchical resolution (R0 through R15) supports country-scale down
>    to ~5-meter-scale with clean parent/child rollup.
>    - Open-source, already widely adopted by geospatial vendors,
>    ride-share, retail planning, and increasingly ad-tech.
>    - Does not depend on political or administrative boundaries — stable
>    across countries, across time, across boundary changes. This matters
>    substantively for longitudinal measurement and for cross-country
>    interoperability.
>
>
> Temporal unit — ISO 8601 week + hour-of-day in UTC, with local-time
> annotation for interpretability. The base time unit being deterministic and
> timezone-unambiguous is critical:
>
>
>    - ISO week is internationally standardized and naturally hierarchical
>    (year → quarter → month → week → day → hour).
>    - UTC sidesteps daylight-saving discontinuities that break clean
>    bucketing.
>    - Hour-of-day UTC paired with a published local-time annotation lets
>    analyses use clock-time semantics without DST corruption.
>    - Calendar context (religious calendars, school sessions, civic
>    events) can be layered as optional metadata that any bucketing scheme can
>    incorporate without changing the base unit.
>
>
> Composite canonical bucket: (H3 cell at declared resolution) × (ISO week)
> × (hour UTC) — with the per-campaign histogramIndex resolving to this
> composite. Coarser buckets (cell × week, or cell × month) work for
> slower-cycle analyses like MMM; finer buckets (cell × week × hour) work for
> day-part-aware causal experiments.
>
> Once the unit is standardized, four things become possible that aren't
> today:
>
>
>    1. Consistent bucketing across campaigns, operators, and measurement
>    paradigms , observational attribution, causal geo experiments per
>    Rick's methodology, MMM, retail-trade-area attribution all use the same
>    units.
>
>
>
>    2. Browser histogram outputs become a shared input for external
>    geo-time-aware models. A geo-RCT outside the browser can consume
>    browser attribution histograms as one of its inputs, with no custom
>    integration per partner.
>
>
>
>    3. Non-browser channels (CTV, OOH, audio, in-store) can
>    geo-time-resolve impressions and conversions to the same units. The
>    browser API becomes a contribution to cross-channel measurement rather than
>    a silo within it.
>
>
>
>    4. Bidirectional integration: browser histograms feed external
>    geo-time experiments; external experiments inform how histograms are
>    bucketed for new campaigns. Browser attribution stops being a one-way
>    endpoint.
>
>
> Importantly, this proposal doesn't require changes to the API surface.
> saveImpression() and measureConversion() can carry composite H3 ×
> ISO-week × hour identifiers as histogramIndex values where the campaign
> chooses; campaigns that prefer other bucketing dimensions can use them. The
> spec would just publish the convention so cross-operator and cross-channel
> composition becomes routine rather than a per-partner custom integration.
>
> The benefit is structural: Attribution Level 1 stops being a siloed
> browser-only attribution layer and becomes the browser-edge contribution to
> a broader cross-channel measurement system. The privacy model - DP, MPC
> aggregation, per-site budgets - stays exactly as it is. The
> compositionality with geo-RCT methodologies, MMM, CTV measurement, OOH
> audience measurement, and retail-trade-area attribution changes
> substantially.
>
> This also addresses the gap Charlie identified , cross-site diversion is
> solved when the randomization unit is geographic rather than
> user-identifier, and it does so without requiring the API or the privacy
> model to change.
>
> I'd be happy to provide more detail on H3 hierarchical structure, ISO week
> conventions, or specific worked examples of how cross-channel composition
> would work mechanically. Happy to discuss further on or off list.
>
> Best,
>
> Nathan Woodman Founder & Principal Advisor, Proof In Data
>
>
>