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

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.


https://www.linkedin.com/pulse/beyond-programmatic-holistic-architecture-agentic-media-williams-3eb9f/
Beyond Programmatic: A Holistic Architecture for Agentic Media
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> 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:
> 
> 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.
> 
> 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.
> 
> 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.
> 
> 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
>