  This paper considers the problem of estimating the unknown intervention
targets in a causal directed acyclic graph from observational and
interventional data. The focus is on soft interventions in linear structural
equation models (SEMs). Current approaches to causal structure learning either
work with known intervention targets or use hypothesis testing to discover the
unknown intervention targets even for linear SEMs. This severely limits their
scalability and sample complexity. This paper proposes a scalable and efficient
algorithm that consistently identifies all intervention targets. The pivotal
idea is to estimate the intervention sites from the difference between the
precision matrices associated with the observational and interventional
datasets. It involves repeatedly estimating such sites in different subsets of
variables. The proposed algorithm can be used to also update a given
observational Markov equivalence class into the interventional Markov
equivalence class. Consistency, Markov equivalency, and sample complexity are
established analytically. Finally, simulation results on both real and
synthetic data demonstrate the gains of the proposed approach for scalable
causal structure recovery. Implementation of the algorithm and the code to
reproduce the simulation results are available at
\url{https://github.com/bvarici/intervention-estimation}.
