no code implementations • 27 Nov 2021 • Md Saiful Islam, Sahil Shikalgar, Md. Noor-E-Alam
Performance on both simulated and real-world data highlights that OAENet notably enhances the accuracy of estimating treatment effects or evaluating policy decision-making with causal inference.
no code implementations • 22 Dec 2020 • Md Saiful Islam, Md Sarowar Morshed, Md. Noor-E-Alam
While causal inference requires randomized experiments, researchers and policymakers are increasingly using observational studies to test causal hypotheses due to the wide availability of observational data and the infeasibility of experiments.
no code implementations • 10 Apr 2019 • Md Mahmudul Hassan, Dizuo Jiang, A. M. M. Sharif Ullah, Md. Noor-E-Alam
Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions.
2 code implementations • 5 Dec 2018 • Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin
However, as we show in this work, there is a typical source of uncertainty that is essentially never considered in observational causal studies: the choice of match assignment for matched groups, that is, which unit is matched to which other unit before a hypothesis test is conducted.
Methodology
no code implementations • 4 Jun 2018 • Dizuo Jiang, Md Mahmudul Hassan, Tasnim Ibn Faiz, Md. Noor-E-Alam
The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers.