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Direct and indirect treatment effects: causal chains and mediation analysis with instrumental variables

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  • Markus Frölich

    (Institute for Fiscal Studies)

  • Martin Huber

    (Institute for Fiscal Studies)

Abstract

This paper discusses the nonparametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment using distinct instruments for the endogenous treatment and the endogenous mediator. We examine different settings to obtain nonparametric identification of (natural) direct and indirect as well as controlled direct effects for continuous and discrete mediators and continuous and discrete instruments. We illustrate our approach in two applications: to disentangle the effects (i) of education on health, which may be mediated by income, and (ii) of the Job Corps training program, which may affect earnings indirectly via working longer hours and directly via higher wages per hour.

Suggested Citation

  • Markus Frölich & Martin Huber, 2014. "Direct and indirect treatment effects: causal chains and mediation analysis with instrumental variables," CeMMAP working papers CWP31/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:31/14
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects From Spillover, Interaction, and General Equilibrium Effects," Rationality and Competition Discussion Paper Series 21, CRC TRR 190 Rationality and Competition.
    2. Strobl, Renate & Wunsch, Conny, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
    3. Lochmann, Alexia & Rapoport, Hillel & Speciale, Biagio, 2019. "The effect of language training on immigrants’ economic integration: Empirical evidence from France," European Economic Review, Elsevier, vol. 113(C), pages 265-296.
    4. Fabian Kosse & Thomas Deckers & Hannah Schildberg-Horisch & Armin Falk, 2016. "The Formation of Prosociality: Causal Evidence on the Role of Social Environment," Working Papers 2016-011, Human Capital and Economic Opportunity Working Group.
    5. Kantorowicz, Jarosław & Köppl–Turyna, Monika, 2019. "Disentangling the fiscal effects of local constitutions," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 63-87.
    6. Christian Dippel & Robert Gold & Stephan Heblich & Rodrigo Pinto, 2017. "Instrumental Variables and Causal Mechanisms: Unpacking the Effect of Trade on Workers and Voters," CESifo Working Paper Series 6816, CESifo Group Munich.
    7. repec:eee:wdevel:v:115:y:2019:i:c:p:258-268 is not listed on IDEAS
    8. Eva Deuchert & Martin Huber & Mark Schelker, 2016. "Direct and Indirect Effects Based on Difference-in-Differences with an Application to Political Preferences Following the Vietnam Draft Lottery," CESifo Working Paper Series 6000, CESifo Group Munich.
    9. Martin Huber, 2016. "Disentangling policy effects into causal channels," IZA World of Labor, Institute of Labor Economics (IZA), pages 259-259, May.
    10. repec:bpj:jecome:v:8:y:2019:i:1:p:27:n:6 is not listed on IDEAS
    11. Huber, Martin & Schelker, Mark & Strittmatter, Anthony, 2019. "Direct and Indirect Effects based on Changes-in- Changes," FSES Working Papers 508, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    12. Tadao Hoshino & Takahide Yanagi, 2018. "Treatment Effect Models with Strategic Interaction in Treatment Decisions," Papers 1810.08350, arXiv.org, revised Oct 2019.
    13. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 01 Oct 2019.
    14. repec:kap:jbuset:v:150:y:2018:i:1:d:10.1007_s10551-016-3134-6 is not listed on IDEAS
    15. repec:eee:forpol:v:98:y:2019:i:c:p:44-53 is not listed on IDEAS
    16. Bijwaard, G.E.; & Jones, A.M.;, 2019. "Education and life-expectancy and how the relationship is mediated through changes in behaviour: a principal stratification approach for hazard rates," Health, Econometrics and Data Group (HEDG) Working Papers 19/05, HEDG, c/o Department of Economics, University of York.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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