  Multiple changes in Earth's climate system have been observed over the past
decades. Determining how likely each of these changes are to have been caused
by human influence, is important for decision making on 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 has
been introduced recently, and was in fact partly used already, in the context
of extreme weather event attribution (EA). We argue that these concepts are
also relevant, and can be straightforwardly extended to, the context of
detection and attribution of long term trends associated to 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
models error, can also be adapted conveniently to this approach. Our proposal
thus allows 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.
