Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Inference in the Presence of Interference in Sponsored Search
  Advertising   In classical causal inference, inferring cause-effect relations from data
relies on the assumption that units are independent and identically
distributed. This assumption is violated in settings where units are related
through a network of dependencies. An example of such a setting is ad placement
in sponsored search advertising, where the clickability of a particular ad is
potentially influenced by where it is placed and where other ads are placed on
the search result page. In such scenarios, confounding arises due to not only
the individual ad-level covariates but also the placements and covariates of
other ads in the system. In this paper, we leverage the language of causal
inference in the presence of interference to model interactions among the ads.
Quantification of such interactions allows us to better understand the click
behavior of users, which in turn impacts the revenue of the host search engine
and enhances user satisfaction. We illustrate the utility of our formalization
through experiments carried out on the ad placement system of the Bing search
engine.

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The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess