Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables
By Markus Frölich, Martin Huber. Social Science Research Network, 2014
123 citations recorded by the source corpus.
The paper discusses the non‐parametric 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 by using distinct instruments for the endogenous treatment and the endogenous mediator. We examine various settings to obtain non‐parametric identification of (natural) direct and indirect as well as controlled direct effects for continuous and discrete mediators and continuous and discrete instruments. We also provide a simulation study and two empirical illustrations.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/bc59ce8368f1fabf9cdeeb682616d675191f099e
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In Statistics and Probability
Citations in the corpus
- Causal mediation analysis in economics: Objectives, assumptions, models (2021, methodology): “(2013); Powdthavee et al. (2013); Burgess et al. (2015); Jhun (2015); Frölich and Huber (2017)), Di erence-in-di erences (see Deuchert, Huber and Schelker 9The assumption given in Pearl(2001) is: Yi(d ′,m) ⊥Mi(d)|Xi = x 10See Pearl (2001), pag.”
- Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, methodology): “Frölich and Huber (2017) use the instrumental variables approach to disentangle the indirect effects through work hours and the direct effects of the program. Hsu, Huber, Lee, and Pipoz (2018) consider the duration of exposure to the program as a continuous treatment and study its effects on behavio”