通过潜在调整发现稀疏反事实因素用于基于调查的社区干预
Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention
AI总结：
本文提出通过潜在调整发现稀疏反事实因素，用于基于调查的社区干预，通过可控调查变量调整转移目标群体向参考群体，采用固定基非负潜在表示进行分布对齐，结合Shapley引导归因和熵正则最优传输差异优化政策可行性。
摘要：
交通调查广泛用于了解出行偏好和采纳障碍，但大多数基于调查的分析仍停留在描述或预测层面，很少提供稀疏、可行的干预策略。本文研究了基于调查响应的稀疏反事实社区干预，目标是通过可控的调查变量调整将目标响应群体向期望的参考群体转移。我们采用固定基非负潜在表示将该任务建模为可行的分布对齐问题，以保持前后可比性并提供从潜在因素到原始变量的稳定映射。为了使潜在移动具有可操作性，通过Shapley引导归因识别目标相关的潜在因素，并将其转移到可控变量作为干预优先级。然后通过最小化熵正则最优传输差异（在干预后目标分布与参考分布之间）以及加权ℓ_{2,1}惩罚项（促进共享政策杠杆稀疏性）来学习可行的群体级调整。在真实世界交通调查数据集上的实验表明，所提出的框架能够生成紧凑且可解释的可行干预措施，具有明确的调整幅度，提高人口层面的转化率，并保持干预稀疏性。代码和数据集可在https://github.com/pangjunbiao/latent-group-alignment.git上公开获取。
英文摘要：
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: https://github.com/pangjunbiao/latent-group-alignment.git
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
