Causal discovery under a confounder blanket. (arXiv:2205.05715v3 [stat.ME] UPDATED)

Inferring causal relationships from observational data is rarely
straightforward, but the problem is especially difficult in high dimensions.
For these applications, causal discovery algorithms typically require
parametric restrictions or extreme sparsity constraints. We relax these
assumptions and focus on an important but more specialized problem, namely
recovering the causal order among a subgraph of variables known to descend from
some (possibly large) set of confounding covariates, i.e. a $\textit{confounder
blanket}$. This is useful in many settings, for example when studying …

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