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What is the value added by using causal machine learning methods in a welfare experiment evaluation?

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  • Strittmatter, Anthony

Abstract

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut’s Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.

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  • Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:labeco:v:84:y:2023:i:c:s0927537123000878
    DOI: 10.1016/j.labeco.2023.102412
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    Cited by:

    1. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2023.

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    More about this item

    Keywords

    Labor supply; Individualized treatment effects; Conditional average treatment effects; Random forest;
    All these keywords.

    JEL classification:

    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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