One of the central problems across the data-driven sciences is of that generalizing experimental findings across changing conditions, for instance, whether a causal distribution obtained from a controlled experiment is valid in settings beyond the study population. While a proper design and careful execution of the experiment can support, under mild conditions, the validity of inferences about the population in which the experiment was conducted, two challenges make the extrapolation step difficult – transportability and sampling selection bias. The former poses the question of whether the domain (i.e., settings, population, environment) where the experiment is realized differs from the target domain in their distributions and causal mechanisms; the latter refers to distortions in the sample’s proportions due to preferential selection of units into the study. In this paper, we investigate the assumptions and machinery necessary for using covariate adjustment to correct for the biases generated by both of these problems, to generalize biased experimental data to infer causal effect in the target domain. We provide complete graphical conditions to determine if a set of covariates is admissible for adjustment. Building on the graphical characterization, we develop an efficient algorithm that enumerates all possible admissible sets with poly-time delay guarantee; this can be useful for when some variables are preferred over the others due to different costs or amenability to measurement.