In early-phase clinical trials, interim monitoring is commonly conducted based on the estimated intent-to-treat effect, which is subject to bias in the presence of noncompliance. To address this issue, we propose a Bayesian sequential monitoring trial design based on the estimation of the causal effect using a principal stratification approach. The proposed design simultaneously considers efficacy and toxicity outcomes and utilizes covariates to predict a patient’s potential compliance behavior and identify the causal effects. Based on accumulating data, we continuously update the posterior estimates of the causal treatment effects and adaptively make the go/no-go decision for the trial. We discuss a motivating smoking cessation example of placebo-controlled randomized phase II clinical trial that aims to evaluate the toxicity and efficacy of a new agent for the treatment of nicotine withdrawal symptoms. Numerical results and sensitivity analysis confirm that the proposed method has desirable operating characteristics and addresses the issue of noncompliance.