  Confounding is a significant obstacle to unbiased estimation of causal
effects from observational data. For settings with high-dimensional covariates
-- such as text data, genomics, or the behavioral social sciences --
researchers have proposed methods to adjust for confounding by adapting machine
learning methods to the goal of causal estimation. However, empirical
evaluation of these adjustment methods has been challenging and limited. In
this work, we build on a promising empirical evaluation strategy that
simplifies evaluation design and uses real data: subsampling randomized
controlled trials (RCTs) to create confounded observational datasets while
using the average causal effects from the RCTs as ground-truth. We contribute a
new sampling algorithm, which we call RCT rejection sampling, and provide
theoretical guarantees that causal identification holds in the observational
data to allow for valid comparisons to the ground-truth RCT. Using synthetic
data, we show our algorithm indeed results in low bias when oracle estimators
are evaluated on the confounded samples, which is not always the case for a
previously proposed algorithm. In addition to this identification result, we
highlight several finite data considerations for evaluation designers who plan
to use RCT rejection sampling on their own datasets. As a proof of concept, we
implement an example evaluation pipeline and walk through these finite data
considerations with a novel, real-world RCT -- which we release publicly --
consisting of approximately 70k observations and text data as high-dimensional
covariates. Together, these contributions build towards a broader agenda of
improved empirical evaluation for causal estimation.
