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Multiple changes in Earth's climate system have been observed over the past decades. Determining how likely each of these changes is to have been caused by human influence is important for decision making with regard to mitigation and adaptation policy. Here we describe an approach for deriving the probability that anthropogenic forcings have caused a given observed change. The proposed approach is anchored into causal counterfactual theory (Pearl 2009), which was introduced recently, and in fact partly used already, in the context of extreme weather event attribution (EA). We argue that these concepts are also relevant to, and can be straightforwardly extended to, the context of detection and attribution of long-term trends associated with climate change (D&A). For this purpose, and in agreement with the principle of fingerprinting applied in the conventional D&A framework, a trajectory of change is converted into an event occurrence defined by maximizing the causal evidence associated to the forcing under scrutiny. Other key assumptions used in the conventional D&A framework, in particular those related to numerical model error, can also be adapted conveniently to this approach. Our proposal thus allows us to bridge the conventional framework with the standard causal theory, in an attempt to improve the quantification of causal probabilities. An illustration suggests that our approach is prone to yield a significantly higher estimate of the probability that anthropogenic forcings have caused the observed temperature change, thus supporting more assertive causal claims.