What is Probabilistic Attribution?

Probabilistic attribution is a method of estimating which ad led to an app install by using indirect signals like device characteristics and behavior, instead of user identifiers.

It works by comparing attributes such as:

• IP address
  
  

• Device model
  
  

• Operating system version
  
  

• Time of click and install
  
  

• User agent

This creates a statistical “fingerprint” that allows attribution systems to make a likely match between a click and a conversion, without needing personal data.

How does it work?

When a user clicks an ad, the attribution platform captures a set of device-level signals. Later, when the app is installed and opened, it captures a similar fingerprint. If the two match closely enough, the install is attributed to that campaign.

Unlike deterministic attribution, this method is not guaranteed — it’s based on probability. Still, it was widely used after iOS 14.5 as a workaround to the loss of IDFA.

However, Apple explicitly prohibits probabilistic attribution on iOS under the ATT framework. If the user does not consent to tracking, apps are not allowed to use fingerprinting to infer attribution. On Android, it’s still allowed (for now).

Why it matters

Probabilistic attribution filled a gap during the industry’s transition to privacy-first models. It allowed teams to:

• Maintain visibility into campaign performance
  
  

• Estimate user quality post-install
  
  

• Operate without full opt-in rates

But its use is increasingly limited, especially on platforms enforcing strict privacy standards. Understanding how it works (and when it’s not allowed) helps marketers avoid relying on methods that may become obsolete or non-compliant.