4 papers
Identification-aware Markov chain Monte Carlo
Toru Kitagawa, Yizhou Kuang
Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-…
Who With Whom? Learning Optimal Matching Policies
Yagan Hazard, Toru Kitagawa
There are many economic contexts where the productivity and welfare performance of institutions and policies depend on who matches with whom. Examples include caseworkers and job s…
Leave No One Undermined: Policy Targeting with Regret Aversion
Toru Kitagawa, Sokbae Lee, Chen Qiu
While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice, often due to legal, fairness or cost concerns. W…
Treatment Choice, Mean Square Regret and Partial Identification
Toru Kitagawa, Sokbae Lee, Chen Qiu
We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-…