Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention

TL;DR

This paper introduces a method to identify sparse, interpretable community interventions from survey data by aligning distributions through latent space adjustments, enhancing policy feasibility and interpretability.

Contribution

It formulates a novel distributional alignment approach using latent representations and optimal transport, enabling effective, sparse community interventions based on survey responses.

Findings

Produces compact, interpretable interventions with explicit adjustment magnitudes.

Improves population-level conversion in transportation survey datasets.

Maintains intervention sparsity and interpretability.

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: https://github.com/pangjunbiao/latent-group-alignment.git