A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference

Md Saiful Islam (), Md Sarowar Morshed () and Md. Noor-E-Alam ()
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Md Saiful Islam: Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Md Sarowar Morshed: Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Md. Noor-E-Alam: Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115

INFORMS Journal on Computing, 2022, vol. 34, issue 6, 3023-3041

Abstract: Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models have been proposed to tackle such uncertainty; however, they produce 0-1 nonlinear problems and lack scalability. In this work, we investigate this emerging data science problem and develop a unique computational framework to solve the robust causal inference test instances from observational data with continuous outcomes. In the proposed framework, we first reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve a globally optimal solution. We perform experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms and compare our results with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process. Finally, the proposed framework not only facilitates robust decision making through big-data causal inference, but it can also be utilized in developing efficient algorithms for other nonlinear optimization problems such as quadratic assignment problems.

Keywords: causal inference; big data; discrete optimization; nonlinear optimization; observational study (search for similar items in EconPapers)
Date: 2022
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