eff2: Efficient Least Squares for Total Causal Effects
Estimate a total causal effect from observational data under
linearity and causal sufficiency. The observational data is supposed to
be generated from a linear structural equation model (SEM) with independent
and additive noise. The underlying causal DAG associated the SEM is required
to be known up to a maximally oriented partially directed graph (MPDAG),
which is a general class of graphs consisting of both directed and
undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such
graphs are usually obtained with structure learning algorithms with added
background knowledge. The program is able to estimate every identified
effect, including single and multiple treatment variables. Moreover, the
resulting estimate has the minimal asymptotic covariance (and hence
shortest confidence intervals) among all estimators that are based on the
sample covariance.
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