Our results are corroborated with experiments.

RCM. Finally, we illustrate the power of this conciliatory perspective by pinpointing

Thus, becoming convoluted and difficult to analyze.

Our focus in this paper is to solve causal inference problems using only observational data and (potentially partial) knowledge on the causal graph of the underlying structural causal model (SCM).

Source code is available at https://github.com/MIV-XJTU/SPEED .

However, in the presence of unobserved confounding, typically the causal quantity of interest will not be point-identified by observational data, unless special mechanisms are present in the data generating process (e.g.

Other concurrent works address, e.g., learning from soft interventions with polynomial mixing [