Tuesday, 16:15 - 16:40 h, Room: MA 004


Nathan Kallus
The power of optimization over randomization in designing controlled trials

Coauthors: Dimitris Bertsimas, Mac A. Johnson


The purpose of a controlled trial is to compare the effects of a proposed drug and a null treatment. Random assignment has long been the standard and aims to make groups statistically equivalent before treatment. By the law of large numbers, as the sample grows, randomized groups grow similar almost surely. However, with a small sample, which is practical reality in many disciplines, randomized groups are often too dissimilar to be useful for any inference at all. To remedy this situation, investigators faced with difficult or expensive sampling usually employ specious assignment schemes to achieve better-matched groups, and without theoretical motivation they then employ probabilistic significance tests, whose validity is questionable. Supplanting probabilistic hypothesis testing with a new theory based on robust optimization, we propose a method we call robust hypothesis testing that assigns subjects optimally and allows for mathematically rigorous inference that does not use probability theory and which is notable for allowing inference with small samples. We provide empirical evidence that suggests that optimization leads to significant advantages over randomization.


Talk 3 of the invited session Tue.3.MA 004
"Applications of robust optimization II" [...]
Cluster 20
"Robust optimization" [...]


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