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Direct and indirect effects of continuous treatments based on generalized propensity score weighting

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  • Martin Huber
  • Yu‐Chin Hsu
  • Ying‐Ying Lee
  • Layal Lettry

Abstract

This paper proposes semi‐ and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables called mediators jointly. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment either given observed covariates or given covariates and the mediator. Our effect estimators are shown to be asymptotically normal when the GPS is estimated by either a parametric or a nonparametric kernel‐based method. We also provide a simulation study and an empirical illustration based on the Job Corps experimental study.

Suggested Citation

  • Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    • Handle: RePEc:wly:japmet:v:35:y:2020:i:7:p:814-840
    DOI: 10.1002/jae.2765
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    Citations

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    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    3. Tübbicke Stefan, 2022. "Entropy Balancing for Continuous Treatments," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 71-89, January.
    4. Martin Huber & Anna Solovyeva, 2020. "Direct and Indirect Effects under Sample Selection and Outcome Attrition," Econometrics, MDPI, vol. 8(4), pages 1-25, December.
    5. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    6. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    8. Yizhen Xu & Numair Sani & AmirEmad Ghassami & Ilya Shpitser, 2021. "Multiply Robust Causal Mediation Analysis with Continuous Treatments," Papers 2105.09254, arXiv.org, revised Oct 2024.
    9. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    10. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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