Targeted Policy Recommendations using Outcome-aware Clustering

Abstract

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.