Evaluating online ad campaigns in a pipeline: causal models at scale

By David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, Diane Lambert. 2010

118 citations recorded by the source corpus.

Subfields: Economics and Econometrics, 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 Economics and Econometrics

1. J. Langford
2. Miroslav Dud'ik
3. Sheng Li
4. Yun Raymond Fu
5. Lihong Li

In Marketing

1. J. Langford
2. Miroslav Dud'ik
3. Yi Chang
4. Xuanhui Wang
5. Donald Metzler
6. Dumitru Erhan
7. Marc Najork
8. Michael Bendersky

In Statistics and Probability

1. Lihong Li
2. Nathan Kallus
3. J. Langford
4. Miroslav Dud'ik
5. Masatoshi Uehara
6. Susan Athey
7. Yi Chang
8. Xuanhui Wang

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).”
• 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].”
• Learning to Rank with Selection Bias in Personal Search (2016, methodology): “Many methods such as propensity matching, inverse propensity weighting, and doubly robust estimation have been applied in online settings [7] with the goal of comparing the effect of a treatment vs.”
• Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis (2015, methodology): “However, the cost of fully randomized experiments is usually very high [10, 25, 35] and in some rich ad treatment circumstances, such fully randomized experiments are even infeasible.”
• Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits (2012, methodology): “tion through replacing pby an estimator p^[16, 24], but it does not allow evaluation of nonstationary policies and does not take advantage of a reward estimator. Doubly Robust (DR) Policy Evaluation [6, 23, 22, 20, 13, 7, 8] incorporates a (possibly biased) reward estimator in the IPS approach accor”
• Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, background): “Causal questions exist in many areas, such as health care [24, 12], economics [14], political science [17], education [36], digital marketing [6, 43, 5, 15, 44], etc.”