Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables

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Learning Neural Causal Models from Unknown Interventions (2019, background): “A few works have attempted to learn structures from unknown-intervention data [10, 11, 12, 13].”
Do-calculus when the True Graph Is Unknown (2015, methodology): “…obtained from heterogeneous data sets, possibly including experimental data or background knowledge, then a search algorithm such as GIES, IOD or a SAT-based procedure may be better (Hauser and B¨uhlmann, 2012; Tillman and Spirtes, 2011; Triantaﬁllou and Tsamardinos, 2014; Hytti-nen et al., 2014).”
Joint Causal Inference from Multiple Contexts (2016, methodology): “The latter approach is commonly used by constraint-based methods for causal discovery from multiple data sets (e.g., Tillman, 2009; Claassen and Heskes, 2010; Tillman and Spirtes, 2011; Hyttinen et al., 2014; Triantafillou and Tsamardinos, 2015; Rothenhäusler et al., 2015; Forré and Mooij, 2018).”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, background): “Tillman and Spirtes (2011) present an algorithm for the same task that handles a limited type of conflicts (those concerning p-values for the same pair of variables stemming from different data sets) by combining the p-values for conditional”
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models (2020, background): “[31, 5] show the identiﬁability of learned causal structure from interventional data.”