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