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Identification of Causal Mechanisms Based on Between-Subject Double Randomization Design

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  • Conny Wunsch
  • Renate Strobl
Abstract
Understanding the mechanisms through which treatment effects come about is crucial for designing effective interventions. The identification of such causal mechanisms is challenging and typically requires strong assumptions. This paper discusses identification and estimation of natural direct and indirect effects in so-called double randomization designs that combine two experiments. The first and main experiment randomizes the treatment and measures its effect on the mediator and the outcome of interest. A second auxiliary experiment randomizes the mediator of interest and measures its effect on the outcome. We show that such designs allow for identification based on an assumption that is weaker than the assumption of sequential ignorability that is typically made in the literature. It allows for unobserved confounders that do not cause heterogeneous mediator effects. We demonstrate estimation of direct and indirect effects based on different identification strategies that we compare to our approach using data from a laboratory experiment we conducted in Kenya.

Suggested Citation

  • Conny Wunsch & Renate Strobl, 2018. "Identification of Causal Mechanisms Based on Between-Subject Double Randomization Design," CESifo Working Paper Series 7142, CESifo.
    • Handle: RePEc:ces:ceswps:_7142
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    1. Strobl, Renate, 2022. "Background risk, insurance and investment behaviour: Experimental evidence from Kenya," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 34-68.

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    More about this item

    Keywords

    direct and indirect effects; causal inference; mediation analysis; identification;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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