Abstract:Transportation surveys are widely used to understand travel preferences and adoption barriers, yet most survey-based analyses remain descriptive or predictive and rarely provide sparse, policy-feasible intervention strategies. We study sparse counterfactual community intervention from survey responses, where the goal is to shift a target respondent group toward a desired reference group through controllable survey-variable adjustments. We formulate this task as a policy-feasible distributional alignment problem using a fixed-basis nonnegative latent representation that preserves pre/post comparability and provides a stable map from latent factors to original variables. To make latent movement actionable, target-relevant latent factors are identified through Shapley-guided attribution and transferred to controllable variables as intervention priorities. Feasible group-level adjustments are then learned by minimizing an entropy-regularized optimal-transport discrepancy between the post-intervention target distribution and the reference distribution, together with a weighted $\ell_{2,1}$ penalty that promotes shared policy-lever sparsity. Experiments on real-world transportation survey datasets show that the proposed framework produces compact and interpretable policy-feasible interventions with explicit adjustment magnitudes, improves population-level conversion, and preserves intervention sparsity. Code and datasets are publicly available at: this https URL
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.04460 [cs.LG] |
| (or arXiv:2605.04460v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04460 arXiv-issued DOI via DataCite (pending registration) |
From: Muhammad Ayub Sabir [view email]
[v1]
Wed, 6 May 2026 03:39:53 UTC (945 KB)
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