Identifiability of the Semiparametric Competing Risks Model With Gumbel Dependence Under Exclusion Restrictions

The non-identifiability of the dependent competing risks model has received a lot of attention as it often renders empirical analysis uninformative. This paper contributes a novel route to establish identifiability under a Gumbel copula model, where the main risk of interest follows the semiparametric Cox proportional hazards model and another dependent risk or dependent censoring is left unspecified. It is shown that the Gumbel copula parameter is identifiable, provided that there exist two covariates that only affects the main risk.