Summary A surrogate marker (S) is a variable that can be measured earlier and often more easily than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well?S?can replace?T?or examining the use of?S?in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of?T?given?S?and?Z. It is well known that such models do not have causal interpretations because the models condition on a postrandomization variable?S. In this article, we directly model the relationship among?T,?S, and?Z?using a potential outcomes framework introduced by Frangakis and Rubin (2002,?Biometrics?58, 21?29). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross-classification of the potential outcomes of?S?and?T?when?S?and?T?are both binary. We use a log-linear model to directly model the association between the potential outcomes of?S?and?T?through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the nonidentifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study.