Generating real-world evidence about the effect of medication discontinuation or dose reduction on outcomes, such as reduction of adverse drug effects (ADE
intended benefit) and occurrence of adverse drug withdrawal events (ADWE
unintended harm), is crucial to informing deprescribing decisions. Determining the causal effects of deprescribing is difficult for many reasons, including lack of randomization in real-world study designs and other design and measurement issues that pose threats to internal validity. The inherent challenge is how to identify the effects, both intended benefits and unintended harms, of a new medication stoppage or reduction when implemented in patients with many potential clinical and social risks that may influence the likelihood of deprescribing as well as outcomes. We discuss methodological issues of estimating the effect of medication discontinuation or reduction on risk of ADEs and ADWEs considering: (1) sampling study populations of sufficient size with the potential to demonstrate clinically meaningful and quantifiable outcomes, (2) accurate and appropriately timed measurement of covariates, outcomes, and discontinuation, and (3) statistical approaches to managing confounding and other biases inherent in long-term medication use by individuals with multiple morbidities. Designing rigorous deprescribing studies that address internal validity threats will support evidence generation by improving the ability to assess benefits and harms when the exposure of interest is the absence of a medication. Iterative learnings about data quality, variable definition, variable measurement, and exposure-outcome associations will inform strategies to improve the causal inferences possible in real-world deprescribing studies.