  To estimate causal effects, analysts performing observational studies in
health settings utilize several strategies to mitigate bias due to confounding
by indication. There are two broad classes of approaches for these purposes:
use of confounders and instrumental variables (IVs). Because such approaches
are largely characterized by untestable assumptions, analysts must operate
under an indefinite paradigm that these methods will work imperfectly. In this
tutorial, we formalize a set of general principles and heuristics for
estimating causal effects in the two approaches when the assumptions are
potentially violated. This crucially requires reframing the process of
observational studies as hypothesizing potential scenarios where the estimates
from one approach are less inconsistent than the other. While most of our
discussion of methodology centers around the linear setting, we touch upon
complexities in non-linear settings and flexible procedures such as target
minimum loss-based estimation (TMLE) and double machine learning (DML). To
demonstrate the application of our principles, we investigate the use of
donepezil off-label for mild cognitive impairment (MCI). We compare and
contrast results from confounder and IV methods, traditional and flexible,
within our analysis and to a similar observational study and clinical trial.
