Corpus ID: 73728448

Estimating Individual Advertising Effect in E-Commerce

@article{Liu2019EstimatingIA,
  title={Estimating Individual Advertising Effect in E-Commerce},
  author={Hao Liu and Yunze Li and Qinyu Cao and Guang Qiu and Jiming Chen},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.04149}
}
Online advertising has been the major monetization approach for Internet companies. Advertisers invest budgets to bid for real-time impressions to gain direct and indirect returns. Existing works have been concentrating on optimizing direct returns brought by advertising traffic. However, indirect returns induced by advertising traffic such as influencing the online organic traffic and offline mouth-to-mouth marketing provide extra significant motivation to advertisers. Modeling and… Expand
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References

SHOWING 1-10 OF 34 REFERENCES
Bid optimizing and inventory scoring in targeted online advertising
TLDR
This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price and combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price. Expand
Causally motivated attribution for online advertising
TLDR
A causally motivated methodology for conversion attribution in online advertising campaigns is presented and it is argued that in cases where causal assumptions are violated, these approximate methods can be interpreted as variable importance measures. Expand
Attribution Modeling Increases Efficiency of Bidding in Display Advertising
TLDR
It is argued that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising and learns and utilizes an attribution model in the bidder itself and shows how it modifies the average bid after a click. Expand
Optimal real-time bidding for display advertising
TLDR
The mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because according to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher. Expand
Optimized Cost per Click in Taobao Display Advertising
TLDR
A bid optimizing strategy called optimized cost per click (OCPC) is proposed which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity and yields substantially better results than previous fixed bid manner. Expand
Lift-Based Bidding in Ad Selection
TLDR
This paper proposes a new bidding strategy and proves that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. Expand
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation
TLDR
A fast and scalable Bayesian nonparametric analysis of heterogenous treatment effects and their measurement in relation to observable covariates and it is argued that practitioners should look to ensembles of trees (forests) rather than individual trees in their analysis. Expand
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
  • Stefan Wager, S. Athey
  • Computer Science, Mathematics
  • Journal of the American Statistical Association
  • 2018
TLDR
This is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference and is found to be substantially more powerful than classical methods based on nearest-neighbor matching. Expand
Combining observational and experimental data to find heterogeneous treatment effects
TLDR
This work proposes a method to combine observational data sets available that are orders of magnitude larger with sufficient experimental data to identify a monotonic, one-dimensional transformation from observationally predicted treatment effects to real treatment effects. Expand
Recursive partitioning for heterogeneous causal effects
  • S. Athey, G. Imbens
  • Mathematics, Economics
  • Proceedings of the National Academy of Sciences
  • 2016
TLDR
This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Expand
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