Advertising Marginal Influence (AMI)
AMIExperimental Counterfactual Measure of Advertising Effect on AI-Mediated Selection Probability
AMI measures advertising's causal effect on AI-mediated selection through experimental counterfactual comparison.
Definition
Advertising Marginal Influence (AMI) measures the causal effect of advertising exposure on AI-mediated selection probability. The canonical formula is AMI = P(choice | advertising exposure) - P(choice | no advertising exposure), where choice probabilities are measured under identical AI recommendations to isolate advertising effect from selection effects. AMI provides an experimental test of the Persuasion Compression hypothesis.
Advertising Marginal Influence quantifies how advertising exposure changes selection probability in AI-mediated discovery. By comparing selection probabilities with and without advertising exposure while holding AI recommendations constant, AMI isolates advertising's causal effect. AMI approaching zero would validate Persuasion Compression: advertising has no effect on AI-mediated selection.
Conceptual Formula
AMI = P(choice | advertising exposure) - P(choice | no advertising exposure). Requires AI Recommendation Control to ensure identical recommendations across exposure conditions. Measures selection probability difference attributable to advertising.What This Index Measures
AMI measures advertising's causal effect on AI-mediated selection.
By definition: AMI is the difference in selection probabilities with and without advertising exposure.
Implications
- AMI provides a testable measure for Persuasion Compression
AMI approaching zero would validate Persuasion Compression.
If advertising exposure does not change selection probability, AI-mediated discovery bypasses advertising influence.
Implications
- Zero AMI would indicate advertising ineffectiveness in AI-mediated contexts
Methodology
Type
index construction
Data Sources
Confidence Level
experimental
Description
AMI = P(choice | advertising exposure) - P(choice | no advertising exposure). Requires AI Recommendation Control to ensure identical recommendations across exposure conditions. Measures selection probability difference attributable to advertising.
Limitations
- Requires experimental design
- AI system behavior may vary
- Selection probability measurement requires instrumentation
Key Takeaways
Key Points
- AMI = P(choice|ad) - P(choice|no ad)
- Experimental counterfactual design
- Tests Persuasion Compression hypothesis
- AI Recommendation Control required
Target Audience
Relevance Tags
Source Paper
Citation
For Advertising Marginal Influence (AMI), see HomeSelf Research (2026), Digital Advertising Costs and AI-Mediated Discovery: An Evidence Synthesis on Zero-Click, Paid Media Dependency, and Customer Acquisition Economics.