Rationale: Continuous positive airway pressure (CPAP), the first line therapy for obstructive sleep apnea (OSA), is considered effective in reducing daytime sleepiness. Its efficacy relies on adequate adherence, often defined as >4 hours per night. However, this binary threshold may limit our understanding of the causal effect of CPAP adherence and daytime sleepiness and multilevel approach for CPAP adherence can be more appropriate. Objective: In this study, we show how two causal inference methods can be applied on observational data for the estimation of the effect of different ranges of CPAP adherence on daytime sleepiness measured by the Epworth sleepiness score (ESS) . Methods: Data were collected from a large prospective observational French cohort for OSA patients. Four groups of CPAP adherence were considered (0-4; 4-6; 6-7 and 7-10 hours per night). Multivariable regression, inverse-probability-of-treatment weighting (IPTW) and IPTW with regression adjustment (IPTW-RA) were used to assess the impact of CPAP adherence level on daytime sleepiness. Results: In this study, 9,244 OSA patients treated by CPAP were included. The mean initial ESS was 11 (±5.2) with a mean reduction of 4 points (±5.1). Overall, there was an evidence of the causal effect of CPAP adherence on daytime sleepiness which was mainly observed between the lower CPAP adherence group (0-4h) compared to the higher CPAP adherence group (7-10h). There are no differences by considering higher level of CPAP adherence (>4h). Conclusion: We showed that IPTW and IPTW-RA can be easily implemented to answer questions regarding causal effects using observational data when randomized trials cannot be conducted. Both methods give a direct causal interpretation at the population-level and allow the assessment of the appropriate consideration of measured confounders.