Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials, RCTs) helps us assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data: First, the treatment assignment mechanism is often unknown for observational data, and second, the sampling mechanism often differs between the RCT and the observational data. Differences in the treatment assignment mechanism introduce potential confounding, whereas differences in the sampling mechanism introduce sampling bias. This article proposes a two-step framework for the validation of causal effect estimates obtained from observational data by adjusting for both mechanisms. A simulation study is conducted to show that our suggested two-step framework enables observational data to produce causal effect estimates similar to those of an RCT. An application of our approach to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated. This article establishes a novel framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.