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

Rick, Chris, all,


The thread has moved quickly and the core proposal is now spread across
several replies. I want to put the full version in one place on the list,
for the record.


The suggestion is narrow. Standardize the unit of analysis. Once the unit
is standardized, the inputs and the experimental designs that consume it
can remain open.


Geography sidesteps the cross-site diversion problem. Every operator sees
the same geographic units: every publisher, every advertiser, every
measurement vendor, every channel including CTV, out-of-home, audio, and
in-store outside the browser. No identity bridging is required, and the
randomization unit is consistent across the ecosystem by construction. But
geography alone is not 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 out-of-home impression at
8:15pm, and a web conversion at 8:30pm only become composable across
channels if they all resolve to the same (cell, time-bin) buckets.

Spatial unit: the Uber H3 hexagonal hierarchical grid, with R8 (roughly 460
meter hex edge) as the default resolution.

   - Hexagonal cells are uniform in neighbor relationships, with none of
   the diagonal-versus-orthogonal asymmetry that square grids carry.
   - Hierarchical resolution (R0 through R15) supports country scale down
   to roughly 5 meters, with clean parent and child rollup.
   - H3 is open source and already widely adopted by geospatial vendors,
   ride-share, retail planning, and increasingly ad tech.
   - Cells do not depend on political or administrative boundaries. They
   are stable across countries, across time, and across boundary changes. This
   matters substantively for longitudinal measurement and for cross-country
   interoperability.


Temporal unit: ISO 8601 week plus hour-of-day in UTC, with a local-time
annotation for interpretability.

   - ISO week is internationally standardized and naturally hierarchical:
   year to quarter to month to week to day to hour.
   - UTC sidesteps the 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 are not
today.

   1. Consistent bucketing across campaigns, operators, and measurement
   paradigms. Observational attribution, causal geo experiments per Rick's
   methodology, MMM, and 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, out-of-home, 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, and external experiments inform how histograms are bucketed
   for new campaigns. To Chris's point, if the hex is in or derived from the
   bid request, the same unit serves bid-time reach and frequency management,
   not only post-bid measurement.


This does not require changes to the API surface. saveImpression() and
measureConversion() can carry composite H3 × ISO-week × hour identifiers as
histogramIndex values where the campaign chooses, and 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 browser-only
attribution layer and becomes the browser-edge contribution to a broader
cross-channel measurement system. The privacy model, DP, MPC aggregation,
and per-site budgets stays exactly as it is. This also addresses the
cross-site diversion gap, because the randomization unit is geographic
rather than user-identifier, and it does so without requiring the API or
the privacy model to change.


Best,
Nathan Woodman

On Thu, Jun 4, 2026 at 9:58 AM Chris Williams <chris_williams65@icloud.com>
wrote:

> Imo - there is no such thing as browser attribution.
>
> Attribution cannot be executed without consideration of non-digital media
> and non-media factors eg price changes.
> Attribution can ONLY occur in the geo-temporal space on a probabilistic
> modelled method.
> Hence the importance of standardized Geo - Canada uses postal codes, much
> smaller than DMAs.
>
> MTA needs to be re-defined as Multi-Touch Analysis - an input into MMM
> that helps to illuminate cross media touchpoints and multi-channel
> dependancies. Look at how Google Meridian uses Search as a confounding
> factor in MMM. No medium works alone.
>
> I’m doing a webinar with CIMM on MMM June 8th where I stress the
> importance of geo (thanks Rick) in the outcome measurement process in
> addition to linking it back to the planning, buying and campaign set up.
>
> c
>
>
> [image: 1780415793990.jpeg]
>
> CIMM is hosting its next Research Seminar, Sharpening the Competitive Edge
> of TV's Ad Proposition, on Monday, June 8 from 2:00–3:30 PM ET. Open to the
> entire industry, the virtual session will… | Coalition for Innovative Media
> Measurement (CIMM)
> <https://www.linkedin.com/posts/cimm-is-hosting-its-next-research-seminar-share-7467605088182861824-1Abx/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAA6vMBieNPQNTL87LwTLuoLySm0oqEr7M>
> linkedin.com
> <https://www.linkedin.com/posts/cimm-is-hosting-its-next-research-seminar-share-7467605088182861824-1Abx/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAA6vMBieNPQNTL87LwTLuoLySm0oqEr7M>
>
> <https://www.linkedin.com/posts/cimm-is-hosting-its-next-research-seminar-share-7467605088182861824-1Abx/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAA6vMBieNPQNTL87LwTLuoLySm0oqEr7M>
>
>
> On Jun 4, 2026, at 8:58 AM, Nathan Woodman <nate@proofindata.com> wrote:
>
> 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.
>>
>>
>> <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
>>
>>
>>
>