Abstract
We present a causal inference framework for evaluating the impact of advertising treatments. Our framework is computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. We illustrate the applicability of our proposal on a novel advertising effectiveness study: finding the best ad size on different mobile devices in order to maximize the success rates. The study shows a surprising phenomenon that a larger mobile device does not need a larger ad. In particular, the 300∗250 ad size is universally good for all the mobile devices, regardless of the mobile device size.
| Original language | English (US) |
|---|---|
| Title of host publication | WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 943-944 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450334730 |
| DOIs | |
| State | Published - May 18 2015 |
| Externally published | Yes |
| Event | 24th International Conference on World Wide Web, WWW 2015 - Florence, Italy Duration: May 18 2015 → May 22 2015 |
Other
| Other | 24th International Conference on World Wide Web, WWW 2015 |
|---|---|
| Country | Italy |
| City | Florence |
| Period | 5/18/15 → 5/22/15 |
Keywords
- Ad Size
- Advertising
- Causal Inference
- Mobile
ASJC Scopus subject areas
- Computer Networks and Communications
- Software