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Title: Using Machine Learning to Examine Heterogeneity of the Effects of Changes in the Earned Income Tax Credit on Child Health and Development
Resulting in 1 citation.
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Rehkopf, David |
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Using Machine Learning to Examine Heterogeneity of the Effects of Changes in the Earned Income Tax Credit on Child Health and Development Presented: Chicago IL, Association for Public Policy Analysis and Management (APPAM) Annual Fall Research Conference, November 2017 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Association for Public Policy Analysis and Management (APPAM) Keyword(s): Child Development; Earned Income Tax Credit (EITC); Heterogeneity; Legislation; Modeling Permission to reprint the abstract has not been received from the publisher. Traditionally, examination of the heterogeneity of treatment effects has proceeded by priors from the literature, and due to power issues generally has examined only a few potential factors leading to heterogeneous effects. At the same time, there have been considerable advances in machine learning algorithms that scan over a large number of covariates to establish models of covariates that best explain a specified outcome, penalizing for greater degrees of freedom that come from multiple comparisons. My analysis uses this approach to examine potential heterogeneity of treatment effects of the largest anti-poverty policy in the United States, the Earned Income Tax Credit. I examine the spatial and temporal changes in the generosity of the policy over time (1986 to 2012) as an exogenous exposure with effects on child development outcomes using data from the 1979 National Longitudinal Survey of Youth. Rather than examining heterogeneity of treatment effects only by basic demographic factors, I using an ensemble machine learning approach (using multiple machine learning algorithms including random forest, Elastic-Net, Least Angle Regression, Support Vector Machine, Bayesian GLM) to examine whether treatment effects differ by several dozen potential demographic, socioeconomic, environmental and behavioral factors. |
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Bibliography Citation
Rehkopf, David. "Using Machine Learning to Examine Heterogeneity of the Effects of Changes in the Earned Income Tax Credit on Child Health and Development." Presented: Chicago IL, Association for Public Policy Analysis and Management (APPAM) Annual Fall Research Conference, November 2017. |