2016 INFORMS Annual Meeting Program

MB12

INFORMS Nashville – 2016

MB12 104B-MCC Joint Session APS/Optimization: Advances in Causal Inference Using Optimization Sponsored: Optimization, Integer and Discrete Optimization Sponsored Session Chair: Nathan Kallus, Cornell University and Cornell Tech, 111 8th Avenue #302, New York, NY, 10011, United States, kallus@cornell.edu Co-Chair: Juan Pablo Vielma, Massachusetts Institute of Technology, Cambridge, MA, United States, jvielma@mit.edu Co-Chair: Jose R. Zubizarreta, Columbia University, New York, NY, United States, zubizarreta@columbia.edu 1 - Multivariate Matching Methods For Causal Inference That Are Balance-variance Pareto Optimal And Optimal Kernel Matching Nathan Kallus, Assistant Professor, Cornell University and Cornell Tech, 111 8th Avenue #302, New York, NY, 10011, United States, kallus@cornell.edu We present minimax and Bayesian optimality criteria for non-parametric matching for causal inference. These lead to extensions of existing methods, including nearest-neighbor (Cochran 1953) and coarsened exact matching (Iacus et al. 2011), that optimally and automatically adjust balance vis-à-vis matched sample variance. We develop a new optimal matching method we call optimal kernel matching (OKM), whose superiority we demonstrate theoretically (optimal rates) and empirically (with real data). We connect our theory to equal percent bias reduction (Rubin 1976), which we generalize to non-linear response functions, showing OKM can achieve uniform error reduction in non-linear settings. 2 - Large-scale Optimal Matching For Design-based Inference Using Integer Programming Jose R. Zubizarreta, Columbia, New York, NY, United States, zubizarreta@columbia.edu, Juan Pablo Vielma In observational studies in business research and empirical operations management, matching methods are often used to approximate the ideal study that would be conducted if it were possible to do it by controlled experimentation. In this paper, we present an alternative approach to matching using integer programming, discuss its theoretical properties, and illustrate its performance in real-world data sets. 3 - Leveraging Multiple Outcomes In Matched Observational Studies Colin Fogarty, Massachusetts Institute of Technology, Cambridge, MA, United States, colin.b.fogarty@gmail.com We demonstrate that when performing multiple comparisons in an observational study, the loss in power from controlling the familywise error rate can, through the solution of a quadratically constrained linear program, be attenuated when assessing the robustness of the study’s findings to unmeasured confounding. We show that this allows for uniform improvements in the power of a sensitivity analysis both for the overall null across outcomes and for outcome-specific null hypotheses when compared to combining individual sensitivity analyses. We illustrate our method through an example examining the impact of smoking on naphthalene levels in the body.

information regarding the liability matrix is revealed. We conduct a new sensitivity analysis to characterize the conditions under which a single bank is solvent, default or bankrupted, and estimate the probability that some financial institute in the network will be bankrupted under mild assumptions on the market shock and the network structure. We also present some numerical experiments to verify the theoretical conclusions in the paper. 3 - A Copositive Perspective On Two-stage Adjustable Robust Linear Programming Guanglin Xu, University of IOWA, guanglin-xu@uiowa.edu We consider a two-stage adjustable robust linear optimization problem in which the right-hand side is uncertain and belongs to a convex and compact uncertainty set. We propose a copositive representation for the two-stage problem. We then provide a tractable inner approximation for the copositive program, which leads to a better performance compared to the well-known affine-rule policy. We show the effectiveness of our approach on several numerical examples. Sponsored: Analytics Sponsored Session Chair: Harrison Schramm, CANA Advisors, 1, Pacific Grove, CA, 93950, United States, harrison.schramm@gmail.com 1 - Robust Non Parametric Tests To Identify Treatment Effects Noor E. Alam. M.D., Northeastern University, md.alam@neu.edu We proposed a number of non-parametric robust testing tools to handle uncertainty in detecting treatment effect from observational studies data. In this work, we present an alternative to the standard non-parametric hypothesis tests by leveraging the power of discrete optimization technique. Its been found that our tests are robust to the choice of experimenter. 2 - Linear Probability Models And Big Data: Prediction, Inference, And Selection Bias Galit Shmueli, National Tsing Hua University, Hsinchu, Taiwan, galit.shmueli@iss.nthu.edu.tw, Suneel Babu Chatla Linear probability models (LPM) - linear regression models applied to a binary outcome - are used in various fields. We perform a simulation study to evaluate the pros and cons of LPMs compared to logit and probit, especially with Big Data. We consider common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We find that coefficient directions, statistical significance and marginal effects yield results similar to logit and probit. LPM coefficients are consistent up to a multiplicative scalar. For classification and selection bias, LPM is on par with logit/probit in terms of class separation and ranking, but lacking for propensities 3 - Managing Brokers For The Sales Of A Complex New Product Vahideh Abedi, California State University Fullerton, Fullerton, CA, United States, vabedi@fullerton.edu, Rahul Bhaskar Firms introducing a new product typically rely on sales efforts of brokers to enhance sales. Customers make their purchase decision not only based on the word of mouth they have received from other customers about the product, but also based on the collective information received from the brokers. Therefore, brokers act synergistically to generate sales while competing. We develop an analytical framework for this sales process and show how it can facilitate important managerial decision making. MB15 104E-MCC Stochastic and First-order Methods for Data Analysis Invited: Modeling and Methodologies in Big Data Invited Session Chair: Guanghui Lan, Gatech, Atlanta, GA, United States, george.lan@isye.gatech.edu 1 - An Optimal Randomized Incremental Gradient Method Yi Zhou, Georgia Institute of Technology, yizhou@gatech.edu We introduce a deterministic primal-dual gradient (PDG) method that can achieve the optimal black-box iteration complexity for solving finite-sum convex optimization problems using a primal-dual termination criterion. We also develop a randomized version (RPDG) method, which needs to compute the gradient of only one randomly selected smooth component at each iteration, but can possibly achieve better complexity than PDG in terms of the total number of gradient evaluations. We also show that the complexity of the RPDG method is not improvable by developing a new lower complexity bound for a general class of randomized methods for solving large-scale finite-sum convex optimization problems. MB14 104D-MCC Data Analytics

MB13 104C-MCC Uncertain Linear Optimization Sponsored: Optimization, Global Optimization Sponsored Session

Chair: Jiming Peng, associate professor, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, United States, jopeng@uh.edu 1 - Assessing Systemic Risk In Financial Market Under Uncertain Liabilities Jiming Peng, University of Houston, jopeng@uh.edu We consider the linear optimization model for assessing the systemic risk in a financial network where only partial information on the coefficient data matrix in available. We develop iterative procedures to identify the worst-case and the best- case. Our theoretical analysis and numerical experiments illustrate that the potential risk caused by the failure of a single bank in the network is much more severe than what’s have been estimated in the literature. 2 - Vulnerability Analysis Of Financial Networks Aein Khabazian, University of Houston, aeinkhabazian@gmail.com Jiming Peng In this paper, we analyze the vulnerability of a financial network based on the linear optimization model introduced by Eisenberg and Noe (2001), where the right hand side of the constraints is subject to market shock and only partial

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