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We consider the problem of recovering the causal structure underlyingobservations from different experimental conditions when the targets of theinterventions in each experiment are unknown. We assume a linear structuralcausal model with additive Gaussian noise and consider interventions thatperturb their targets while maintaining the causal relationships in the system.Different models may entail the same distributions, offering competing causalexplanations for the given observations. We fully characterize this equivalenceclass and offer identifiability results, which we use to derive a greedyalgorithm called GnIES to recover the equivalence class of the data-generatingmodel without knowledge of the intervention targets. In addition, we develop anovel procedure to generate semi-synthetic data sets with known causal groundtruth but distributions closely resembling those of a real data set of choice.We leverage this procedure and evaluate the performance of GnIES on synthetic,real, and semi-synthetic data sets. Despite the strong Gaussian distributionalassumption, GnIES is robust to an array of model violations and competitive inrecovering the causal structure in small- to large-sample settings. We provide,in the Python packages "gnies" and "sempler", implementations of GnIES and oursemi-synthetic data generation procedure.

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions