<p>This study investigates statistical inference for a competing risks model with partially observed dependent causes of failure, specifically in the context of left truncated and right censored data. Dependence between failure causes is modeled using the Marshall-Olkin bivariate Kumaraswamy distribution. The maximum likelihood estimators of parameters are obtained and subsequently approximate confidence intervals are constructed. Further Bayes method is used to derive both point and interval estimators of parameters. Extensive simulation studies are conducted to evaluate the performance of all estimators. Useful applications of the proposed methodology are demonstrated through the analysis of two empirical datasets. These applications underscore the versatility and applicability of the proposed inferential procedures in complex reliability and biomedical settings.</p>