Targeted Policy Recommendations using Outcome-aware Clustering

Ananth Balashankar, Samuel Fraiberger, Eric M. Deregt, Marelize Görgens, Lakshminarayanan Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Policy recommendations using observational data typically rely on estimating an econometric model on a sample of observations drawn from an entire population. However, different policy actions could potentially be optimal for different subgroups of a population. In this paper, we propose outcome-aware clustering, a new methodology to segment a population into different clusters and derive cluster-level policy recommendations. Outcome-aware clustering differs from conventional clustering algorithms across two basic dimensions. First, given a specific outcome of interest, outcome-aware clustering segments the population based on selecting a small set of features that closely relate with the outcome variable. Second, the clustering algorithm aims to generate near-homogeneous clusters based on a combination of cluster size-balancing constraints, inter and intra-cluster distances in the reduced feature space. We generate targeted policy recommendations for each outcome-aware cluster based on a standard multivariate regression of a condensed set of actionable policy features (which may partially overlap or differ from the features used for segmentation) from the observational data. We implement our outcome-aware clustering method on the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) dataset to generate targeted policy recommendations for improving farmers outcomes in sub-Saharan Africa. Based on a detailed analysis of the LSMS-ISA, we derive outcome-aware clusters of farmer populations across three sub-Saharan African countries and show that the targeted policy recommendations at the cluster level significantly differ from policies that are generated at the population level.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9781450393478
StatePublished - Jun 29 2022
Event4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022 - Virtual, Online, United States
Duration: Jun 29 2022Jul 1 2022

Publication series

NameACM International Conference Proceeding Series
VolumePar F180472


Conference4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
Country/TerritoryUnited States
CityVirtual, Online

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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