  Conventional methods in causal effect inferencetypically rely on specifying a
valid set of control variables. When this set is unknown or misspecified,
inferences will be erroneous. We propose a method for inferring average causal
effects when all potential confounders are observed, but thecontrol variables
are unknown. When the data-generating process belongs to the class of acyclical
linear structural causal models, we prove that themethod yields asymptotically
valid confidence intervals. Our results build upon a smooth characterization of
linear directed acyclic graphs. We verify the capability of the method to
produce valid confidence intervals for average causal effects using synthetic
data, even when the appropriate specification of control variables is unknown.

Methodology;Machine Learning;Machine Learning