A latent adjustment method identifies sparse counterfactual factors and computes minimal feasible survey-variable changes to align target respondent distributions with reference groups using entropy-regularized optimal transport and weighted l2,1 sparsity.
Uncov- ering distinct public transport user profiles and the factors influencing the users’ intentions
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Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention
A latent adjustment method identifies sparse counterfactual factors and computes minimal feasible survey-variable changes to align target respondent distributions with reference groups using entropy-regularized optimal transport and weighted l2,1 sparsity.