Many research questions involving causal inference are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of 'direct' versus 'indirect' effects comes to play. Disentangling direct and indirect effects may be a difficult task, because the intermediate outcome is generally not under experimental control. We tackle this problem by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our framework: we adopt a principal stratification approach, and we mainly focus on principal strata effects, avoiding to involve a priori counterfactual outcomes. Using non parametric identification strategies, we provide a set of assumptions, which allow us to partially identify the causal estimands of interest, the Principal Strata Direct Effects. Large sample bounds for various Principal Strata average Direct Effects are provided, and a simple hypothetical example is used to show how our augmented design can be implemented and how the bounds can be calculated. Finally our augmented design is compared with and contrasted to a standard randomized design. [Working Paper 2010/04, Department of Statistics, University of Florence, Italy]

Augmented designs to assess principal strata direct effects / A. Mattei; F. Mealli. - ELETTRONICO. - (2010).

Augmented designs to assess principal strata direct effects

MATTEI, ALESSANDRA;MEALLI, FABRIZIA
2010

Abstract

Many research questions involving causal inference are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of 'direct' versus 'indirect' effects comes to play. Disentangling direct and indirect effects may be a difficult task, because the intermediate outcome is generally not under experimental control. We tackle this problem by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our framework: we adopt a principal stratification approach, and we mainly focus on principal strata effects, avoiding to involve a priori counterfactual outcomes. Using non parametric identification strategies, we provide a set of assumptions, which allow us to partially identify the causal estimands of interest, the Principal Strata Direct Effects. Large sample bounds for various Principal Strata average Direct Effects are provided, and a simple hypothetical example is used to show how our augmented design can be implemented and how the bounds can be calculated. Finally our augmented design is compared with and contrasted to a standard randomized design. [Working Paper 2010/04, Department of Statistics, University of Florence, Italy]
2010
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/445653
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