糖心TV

Title to be advised.

Title: Inference with few treated units

Abstract: In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods that rely on asymptotic theory may be unreliable, even when the total number of units is large. This survey reviews and categorizes inference methods that are designed to accommodate few treated units. We consider both cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample validity of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.

Title: Screening Knowledge

A principal (she) tests an agent's (he) knowledge of a subject matter. She has preferences over his unobserved quality, which is correlated with his knowledge. Modeling knowledge as beliefs over an unknown state, I show that optimal tests are simple: They take the form of True-False, weighted True-False or True-False-Unsure, regardless of the principal's preferences, the distribution of the agent's beliefs, its correlation with his quality or his knowledge thereof. The need to elicit knowledge forces the principal to trade-off the efficacy of the test in terms of whom it rewards, against how much it rewards them. The optimal resolution of this trade-off may lead to a partial penalty for an "obvious" answer even if it is correct, a partial reward for a "counterintuitive" answer even if it is incorrect, or a reward for admitting ignorance. When the principal can pick the subject matter, she picks one that admits no such obvious answers. In this case, the highly prevalent True-False test is always optimal, regardless of principal's preferences, agent's learning, or the specific optimal choice of the subject matter.