Identification and Likelihood Inference for Recursive Linear Models with Correlated Errors

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Sequences of regressions and their independences (2011, methodology): “Estimation methods for SEMs were discussed in the Berkeley symposia on mathematical statistics and probability from 1945 to 1965, but some issues still need to be settled; see for instance Drton, Eichler and Richardson (2009), Stanghellini and Wermuth (2005).”
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders (2018, unknown): “More sophisticated constraint-based [5,25,32,34] and score-based approaches [4,7,8,10,11] can deal with latent confounders in the acyclic case.”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “The parameters are optimized using a Residual Iterative Conditional Fitting algorithm [46].”
Global identifiability of linear structural equation models (2010, methodology): “this stepwise inversion is closely related to the idea of pseudo-variable regression used in the iterative conditional fitting algorithm of [6].”