Because of the time and expense required to obtain clinical outcomes of interest, clinical trials often focus on the effects of treatment on more easily obtained “surrogate markers”. Definitions of surrogate markers have been developed in the causal inference framework, in either the “causal effect” or “causal association” settings. In the causal association setting, high-quality surrogate markers have large treatment effects on the outcome when there are large treatment effects on the marker, and vice-versa. A particularly important feature of a surrogate marker is that the direction of a treatment effect be the same for both the marker and the outcome. Settings in which the marker and outcome are positively associated but the marker and outcome have treatment effects in the opposite direction have been referred to as “surrogate paradoxes”. We propose assessing the risk of the surrogate paradox using the meta-analytic causal association framework, by estimating probability that a new treatment will yield treatment effects in different directions between the marker and the outcome, either overall or as a function of the size of a beneficial effect of the treatment on the marker.