Following in the footsteps of the literature on empirical welfare maximization, this paper wants to contribute by stressing the policymaker perspective via a practical illustration of an optimal policy assignment problem. More specifically, by focusing on the class of threshold-based policies, we first set up the theoretical underpinnings of the policymaker selection problem, to then offer a practical solution to this problem via an empirical illustration using the popular LaLonde (1986) training program dataset. The paper proposes an implementation protocol for the optimal solution that is straightforward to apply and easy to program with standard statistical software
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the ...
Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome be...
One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform p...
www.princeton.edu/∼noahw/ We study the problem of a policymaker who seeks to set policy optimally in...
We consider the problem of learning treatment (or policy) rules that are externally valid in the sen...
This work proposes an approach based on reward shaping techniques in a reinforcement learning setti...
Standard experimental designs are geared toward point estimation and hypothesis testing, while bandi...
Much of the focus on finding good representations in reinforcement learning has been on learning com...
This thesis is devoted to designing and analyzing statistical decision rules to improve public polic...
Thesis (Ph.D.)--University of Washington, 2022Sequential decision making, especially in the face of ...
Psychologists are interested in developing instructional policies that boost student learning. An in...
In the economics literature there are two dominant approaches for solving models with optimal experi...
Research on the implications of learning-by-doing has typically been restricted to specifications of...
We consider inference on optimal treatment assignments. Our methods are the first to allow for infere...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the ...
Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome be...
One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform p...
www.princeton.edu/∼noahw/ We study the problem of a policymaker who seeks to set policy optimally in...
We consider the problem of learning treatment (or policy) rules that are externally valid in the sen...
This work proposes an approach based on reward shaping techniques in a reinforcement learning setti...
Standard experimental designs are geared toward point estimation and hypothesis testing, while bandi...
Much of the focus on finding good representations in reinforcement learning has been on learning com...
This thesis is devoted to designing and analyzing statistical decision rules to improve public polic...
Thesis (Ph.D.)--University of Washington, 2022Sequential decision making, especially in the face of ...
Psychologists are interested in developing instructional policies that boost student learning. An in...
In the economics literature there are two dominant approaches for solving models with optimal experi...
Research on the implications of learning-by-doing has typically been restricted to specifications of...
We consider inference on optimal treatment assignments. Our methods are the first to allow for infere...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the ...
Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome be...
One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform p...