Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs. While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves. In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling. Our analysis motivates the idea of weighting the log loss with the business value of the predicted outcome. We present and analyze a new cost weighting scheme and show that significant gains in offline and online performance can be achieved.Comment: First version of the paper was presented at NIPS 2015 Workshop on E-Commerce: https://sites.google.com/site/nips15ecommerce/papers Third version of the paper will be presented at AdKDD 2017 Workshop: adkdd17.wixsite.com/adkddtargetad201

The health costs of ethnic distance: Evidence from Sub-Saharan Africa

We show that ethnic distances can explain the ethnic inequalities in child mortality rates in Africa. Using individual level micro data from DHS surveys for fourteen Sub-Saharan African countries combined with a novel high resolution dataset on the spatial distribution of ethnic groups we show that children whose mothers have a higher linguistic distance from their neighbours have a higher probability of dying. Fractionalization reduces the probability of child death. We argue that fractionalization re ects a higher stock of knowledge and information leading to better health outcomes. Knowledge does not ow smoothly to linguistically distant groups. Linguistically distant mothers also have a lower probability of knowing about the oral rehydration product (ORS) for treating children with diarrhoea

The political economy of the Maoist conflict in India : an empirical analysis

This paper contributes to a burgeoning literature that uses sub-national micro data to identify the causes of civil conflicts. In particular, we study the Maoist/Naxalite conflict in India by constructing a comprehensive district level database using conflict data from four different terrorism databases and combining it with socioeconomic and geography data from myriad sources. In addition to exploiting the within country regional heterogeneity, we use the micro structure of the data to construct group-level characteristics. Using data on 360 districts for 3 time periods, we find evidence on how land inequality and lower incomes are important for the Maoist conflict. Moreover, making use of the micro structure of the data we are able to ask whether exclusion of the low castes and tribes from the growth story of India is important. We find that while the income levels of the different ethnic groups are not important, the growth of incomes of Scheduled Tribes significantly decreases the intensity of the conflict. Finally, we show how historical property rights institutions from colonial times that go back centuries can affect present day conflict outcomes through their impact on economic outcomes, social relations and the political environment in the distric