Handling Correlation in Stacked Difference-in-Differences Estimates with Application to Medical Cannabis Policy


Health policy researchers often have questions about the effects of a policy implemented at some cluster-level unit, e.g., states, counties, hospitals, etc., on individual-level outcomes collected over multiple time periods. Stacked difference-in-differences is an increasingly popular way to estimate these effects. The approach involves estimating treatment effects for each policy-implementing unit, then, if scientifically appropriate, aggregating them to an average effect estimate. However, when individual-level data are available, and non-implementing units are used as comparators for multiple policy-implementing units, data from untreated individuals may be used across multiple analyses, thereby inducing a correlation between effect estimates. Existing methods do not quantify or account for this sharing of controls. A stacked difference-in-differences study is described, investigating the effects of state medical cannabis laws on treatment for chronic pain, a framework for estimating and managing this correlation due to shared control individuals is discussed, and how accounting for it affects the substantive results is shown.

Dec 17, 2023 1:50 PM — 3:30 PM
Berlin, DE