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Direct and Indirect Effects Based on Changes-in-Changes

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  • Martin Huber
  • Mark Schelker
  • Anthony Strittmatter

Abstract

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the condition that the outcome is monotonic in the unobserved heterogeneity and that the distribution of the latter does not change over time conditional on the treatment and the mediator. We also provide a simulation study and two empirical applications regarding a training programme evaluation and maternity leave reform.

Suggested Citation

  • Martin Huber & Mark Schelker & Anthony Strittmatter, 2019. "Direct and Indirect Effects Based on Changes-in-Changes," CESifo Working Paper Series 7855, CESifo.
    • Handle: RePEc:ces:ceswps:_7855
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    References listed on IDEAS

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    Cited by:

    1. Doerr Annabelle & Strittmatter Anthony, 2021. "Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 67-88, January.
    2. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.
    3. Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.

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    More about this item

    Keywords

    direct effects; indirect effects; mediation analysis; changes-in-changes; causal mechanisms; treatment effects;
    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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