  A common issue in learning decision-making policies in data-rich settings is
spurious correlations in the offline dataset, which can be caused by hidden
confounders. Instrumental variable (IV) regression, which utilises a key
unconfounded variable known as the instrument, is a standard technique for
learning causal relationships between confounded action, outcome, and context
variables. Most recent IV regression algorithms use a two-stage approach, where
a deep neural network (DNN) estimator learnt in the first stage is directly
plugged into the second stage, in which another DNN is used to estimate the
causal effect. Naively plugging the estimator can cause heavy bias in the
second stage, especially when regularisation bias is present in the first stage
estimator. We propose DML-IV, a non-linear IV regression method that reduces
the bias in two-stage IV regressions and effectively learns high-performing
policies. We derive a novel learning objective to reduce bias and design the
DML-IV algorithm following the double/debiased machine learning (DML)
framework. The learnt DML-IV estimator has strong convergence rate and
$O(N^{-1/2})$ suboptimality guarantees that match those when the dataset is
unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV
regression benchmarks and learns high-performing policies in the presence of
instruments.
