A bi-weekly challenge from Andre Mirabelli & Opturo (September 28, 2020)

How does an attribution modeler address the following challenge?
Which are better for ex-post attribution:
Temporal smoothing algorithms that change the impact of decisions made on Monday due to results achieved on the subsequent Friday, as the smoothing methods of Carino and Menchero do, or temporal smoothing algorithms that can attribute non-zero impact to null decisions that have the fund perfectly match what the benchmark did, as the method of Frangello does?