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Found 5 papers, 1 papers with code
A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
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.
A Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference
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.
Resilient Supplier Selection in Logistics 4.0 with Heterogeneous Information
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.
A robust approach to quantifying uncertainty in matching problems of causal inference
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
A Possibility Distribution Based Multi-Criteria Decision Algorithm for Resilient Supplier Selection Problems
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.
