For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.

Explaining a complex system through their cause and e ff ect relations is one of the fundamental challenges in science.

We would like to thank the reviewers for their comments and constructive feedback. Below, we address the main issues raised and clarify some misunderstandings. Also, the work of Y ang et al. (2018) characterizes soft interventions in systems without latent variables. Mooij et al. (2013) discussed interventions of this nature in the context of equilibrium in cyclic causal models. Usage of MAGs: The reviewer's observation only holds for hard interventions.

Identifying causal effects is one prominent task throughout empirical sciences.

Identifying causal effects is one prominent task throughout empirical sciences.

Structural Causal Models (SCM) framework introduced by Pearl [2009].

Structural Causal Models (SCM) framework introduced by Pearl [2009].

(Theorem 1). A.1 Graphical representation Theorem 1. V ertex X is removable in a MAG M over the variables V, if and only if 1. for any Y 2 Adj ( X) and Z 2 Ch ( X) [ N ( X) \{ Y }, Y and Z are adjacent, and 2. Let H denote the induced subgraph of M over V \{ X } . Since X is removable in M, by definition of removability, ( Y? M, Lemma 6 implies that u is not m-connecting relative to W in H . (: Lemma 6 implies that u is not m-connecting relative to W in M . This contradiction proves that X cannot have a descendant in { Y,Z }[ W, which implies that X blocks u in M .