  Discovering the causal effect of a decision is critical to nearly all forms
of decision-making. In particular, it is a key quantity in drug development, in
crafting government policy, and when implementing a real-world machine learning
system. Given only observational data, confounders often obscure the true
causal effect. Luckily, in some cases, it is possible to recover the causal
effect by using certain observed variables to adjust for the effects of
confounders. However, without access to the true causal model, finding this
adjustment requires brute-force search. In this work, we present an algorithm
that exploits auxiliary variables, similar to instruments, in order to find an
appropriate adjustment by a gradient-based optimization method. We demonstrate
that it outperforms practical alternatives in estimating the true causal
effect, without knowledge of the full causal graph.
