Economics Seminar - New results on minimax regret treatment rules in finite samples

We study minimax regret treatment rules in finite samples under matched treatment
assignment in a setup where a policymaker, informed by a sample, needs to decide between T
different treatments for a T≥2. Randomized rules are allowed for. We show that the
generalization of the minimax regret rule derived in Stoye (2009) for the case T = 2 is minimax
regret for general finite T > 2. We also show by example, that in the case of random assignment
the generalization of the minimax rule in Stoye (2009) to the case T > 2 is not necessarily minimax
regret and derive minimax regret rules for a few small sample cases, e.g. for N = 2 when T = 3.
We also discuss numerical approaches to approximate minimax regret rules for unbalanced panels.
We then study minimax regret treatment rules in finite samples when a specific quantile (rather than expected outcome)
is the object of interest. We establish that all treatment rules are minimax regret under "matched" and "random sampling" schemes while under "testing an
innovation" no-data rules are shown to be minimax regret.

The talk is based on multiple papers, two of which

• "Minimax regret treatment rules with finite samples when a quantile is the object of interest" (with Nihal Mehta and Nikita Pavlov)
• "A note on minimax regret rules with multiple treatments in finite samples" (with Haoning Chen)
can be found at
https://patrikguggenberger.wordpress.com/about/