More bang for their bucks: assessing new features for online advertisers

By Diane Lambert, Daryl Pregibon. 2007

27 citations recorded by the source corpus.

Subfields: Marketing, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Marketing

In Statistics and Probability

Citations in the corpus

Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, positive): “Such counterfactual methods have shown promise in a few important Web applications recently like advertising [5, 7, 19, 20, 30], content recommendation [22].”
Doubly Robust Policy Evaluation and Learning (2011, background): “More recently, it has been used in Internet advertising to estimate the effects of new features for online advertisers (Lambert & Pregibon, 2007; Chan et al., 2010).”
Learning from Logged Implicit Exploration Data (2010, background): “Lambert and Pregibon [4] provide a good explanation o f pr pensity scoring in an Internet advertising setting.”
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits (2012, background): “Prominent examples are the ad display problems at Internet advertising engines [15, 7], content recommendation on Web portals [18], as well as adaptive medical treatments.”
Research Dilemmas with Behavioral Big Data (2017, unknown): “While PSM can handle these issues, Lambert and Pregibon 44 note: Our main reservation about all variants of matching is the degree of care required in building the propensity score model and the degree to which the matched sets must balance the ad-vertiser characteristics.”
Toward Predicting the Outcome of an A/B Experiment for Search Relevance (2015, methodology): “As explained in Section 3, the approach we take here is closely related to previous oﬄine evaluation work for recommender and advertising systems based on contextual ban-dits [18, 21], and to causal inference in statistics [17] as well.”
Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study (2015, methodology): “Such counterfactual methods have shown promise in a few important Web applications recently [4, 5, 17, 18, 21, 29, 30, 25].”