We show that the learning of (uncertain) conditional and/or causal information may be modelled by (Jeffrey) imaging on Stalnaker conditionals. We adapt the method of learning uncertain conditional information proposed in Günther (2017) to a method of learning uncertain causal information. The idea behind the adaptation parallels Lewis (1973c)’s analysis of causal dependence. The combination of the methods provides a unified account of learning conditional and causal information that manages to clearly distinguish between conditional, causal and conjunctive information. Moreover, our framework seems to be the first general solution that generates the correct predictions for Douven (2012)’s benchmark examples and the Judy Benjamin Problem.