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

Author

Listed:
  • Hsu, Yu-Chin
  • Huber, Martin
  • Lee, Ying-Ying
  • Pipoz, Layal

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 or mediators. 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 application to the Job Corps program.

Suggested Citation

  • Hsu, Yu-Chin & Huber, Martin & Lee, Ying-Ying & Pipoz, Layal, 2018. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," FSES Working Papers 495, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    • Handle: RePEc:fri:fribow:fribow00495
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    4. 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.
    5. Ying-Ying Lee & Chu-An Liu, 2024. "Lee Bounds with a Continuous Treatment in Sample Selection," Papers 2411.04312, arXiv.org.
    6. 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.
    7. Tübbicke Stefan, 2022. "Entropy Balancing for Continuous Treatments," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 71-89, January.
    8. Martin Huber & Anna Solovyeva, 2020. "Direct and Indirect Effects under Sample Selection and Outcome Attrition," Econometrics, MDPI, vol. 8(4), pages 1-25, December.
    9. Michel F. C. Haddad & Martin Huber & Lucas Z. Zhang, 2024. "Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning," Papers 2410.21105, arXiv.org.
    10. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    11. 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.
    12. 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

    Keywords

    Mediation; direct and indirect effects; continuous treatment; weighting; generalized propensity score;
    All these keywords.

    JEL classification:

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

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