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2020 Honors Thesis: Miaomiao Zhang


Miaomiao Zhang

Title of Thesis:

Algorithmic Targeting of Social Welfare Programs: Machine Learning for Prediction Model Design and Causal Effects Estimation


Stephen O'Connell, Shomu Banerjee, Seunghwa Rho (QTM)


Governments and aid organizations in developing countries implement algorithmic rules to identify and provide necessary aid for households in underprivileged conditions. Given demographic and background characteristics from administrative data, traditional econometric methods along with regularized linear regressions have been used for targeting social welfare programs. Non-parametric machine learning techniques, however, are less common in these contexts. In this paper, I compare non-parametric forests to parametric linear regression techniques in both prediction and causal treatment effects estimation problem settings. The standard metric of prediction accuracy suggests that random forests perform slightly better than regularized linear regressions, validated across multiple subsets of data; the estimated average treatment effects using both modeling techniques are positive, with only causal forests showing statistically significant results. There is no evidence of significant heterogeneity in individual treatment effects.

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