  Staggered treatment adoption arises in the evaluation of policy impact and
implementation in a variety of settings. This occurs in both randomized
stepped-wedge trials and non-randomized quasi-experimental designs using causal
inference methods based on difference-in-differences analysis. In both
settings, it is crucial to carefully consider the target estimand and possible
treatment effect heterogeneities in order to estimate the effect without bias
and in an interpretable fashion. This paper proposes a novel non-parametric
approach to this estimation for either setting. By constructing an estimator
using two-by-two difference-in-difference comparisons as building blocks with
arbitrary weights, the investigator can select weights to target the desired
estimand in an unbiased manner under assumed treatment effect homogeneity, and
minimize the variance under an assumed working covariance structure. This
provides desirable bias properties with a relatively small sacrifice in
variance and power by using the comparisons efficiently. The method is
demonstrated on toy examples to show the process, as well as in the re-analysis
of a stepped wedge trial on the impact of novel tuberculosis diagnostic tools.
A full algorithm with R code is provided to implement this method. The proposed
method allows for high flexibility and clear targeting of desired effects,
providing one solution to the bias-variance-generalizability tradeoff.
