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Unit Selection: Learning Benefit Function from Finite Population Data
Ang Li · Song Jiang · Yizhou Sun · Judea Pearl
Event URL: https://openreview.net/forum?id=YbPuwbl-yg »

The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if incentivized and a different way if not. The unit selection problem consists of evaluation and search subproblems. Li and Pearl defined the "benefit function" to evaluate the average payoff of selecting a certain individual with given characteristics. The search subproblem is then to design an algorithm to identify the characteristics that maximize the above benefit function. The hardness of the search subproblem arises due to the large number of characteristics available for each individual and the sparsity of the data available in each cell of characteristics. In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics. Therefore, we could easily obtain the characteristics that maximize the benefit function.

Author Information

Ang Li (University of California, Los Angeles)
Song Jiang (University of California, Los Angeles)
Yizhou Sun (UCLA)
Judea Pearl (UCLA)

Judea Pearl is a professor of computer science and statistics at UCLA. He is a graduate of the Technion, Israel, and has joined the faculty of UCLA in 1970, where he conducts research in artificial intelligence, causal inference and philosophy of science. Pearl has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000;2009), the latter won the Lakatos Prize from the London School of Economics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of the IEEE, AAAI and the Cognitive Science Society. Pearl received the 2008 Benjamin Franklin Medal from the Franklin Institute and the 2011 Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion's Harvey Prize and the ACM Alan M. Turing Award.

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