In causal mediation analysis, natural effects are identified only under strict assumptions involving cross-world counterfactuals. An alternative approach recently developed, called separable, allows for identification of mediational effects in a wide range of models, since it relies on weaker assumptions than those required by natural effects. In this paper, the separable-effect approach is revised and an application to data is presented.

Chiara Di Maria (2021). Does self-efficacy influence academic results? A separable-effect mediation analysis. In C. Perna, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of Short Papers - SIS 2021 (pp. 1382-1387).

Does self-efficacy influence academic results? A separable-effect mediation analysis

Chiara Di Maria
2021

Abstract

In causal mediation analysis, natural effects are identified only under strict assumptions involving cross-world counterfactuals. An alternative approach recently developed, called separable, allows for identification of mediational effects in a wide range of models, since it relies on weaker assumptions than those required by natural effects. In this paper, the separable-effect approach is revised and an application to data is presented.
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Chiara Di Maria (2021). Does self-efficacy influence academic results? A separable-effect mediation analysis. In C. Perna, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of Short Papers - SIS 2021 (pp. 1382-1387).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/513768
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