\section{Conclusions}
Causal effect estimation is a common inference problem across different applications, many of which do not have a known causal diagram describing the system of variables. In this paper, we study the problem of partial identification with knowledge of a Markov equivalence class, represented by a Partial Ancestral Graph (PAG), that can be inferred from data. We demonstrate that analytical bounds can be derived by exploiting the invariances present in the PAG and the corresponding decomposition of causal effects. These are the first bounds on causal effects in the literature that exploit the knowledge encoded in a PAG. We further consider enumeration techniques, that list relevant causal diagrams and apply partial identification techniques on each one separately. We show that despite several redundancies within the space of ME causal diagrams, the computational cost likely remains large in practice. %Ultimately, with these results we hope to widen the practice of causal effect inference without the necessity of making strong assumptions about the domain of interest. In such scenarios, the expectation (or rather hope) is that causal effects could be \emph{partially} identifiable, that is that non-trivial bounds on causal effects can be inferred. 