arXiv subjects

Maxwell Rosenthal

Publications and source records attributed to Maxwell Rosenthal.

3 recordsLinked to original sources

arXiv2025

Identification Design

This paper develops a model of \textit{identification design} and applies it to robust causal inference in microeconometrics. The decision maker observes the population distribution of signals generated by an information structure and ranks actions by their worst-case payoff over the set of admissible state distributions consistent with those signals. We call an environment \textit{manipulable} if every action is implementable under all true distributions of the state variable, and show this holds if and only if all actions share the same worst-case payoff. We confirm in application that all treatment-effects models are manipulable, and moreover that manipulation is feasible via \textit{almost fully informative} information structures that conceal at most one dimension of information from the decision maker. As in practice, we consider a restriction to \textit{marginal information structures} that disclose the joint distribution of the outcome variable, treatment variable, and a selection of covariates. In that context, we provide necessary and sufficient conditions for exact identification and sharp payoff bounds for disclosures that do not satisfy those conditions. In doing so, we show that the disclosure of a sufficiently rich set of covariates to verify faithful execution of the assignment mechanism eliminates all scope for manipulation in experiments, while observational studies remain partially manipulable via covariate selection.

econ.TH↗

arXiv2025

Prior-Free Blackwell

This paper develops a prior-free model of data-driven decision making in which the decision maker observes the entire distribution of signals generated by a known experiment under an unknown distribution of the state variable and evaluates actions according to their worst-case payoff over the set of state distributions consistent with that observation. We show how our model applies to partial identification in econometrics and propose a ranking of experiments in which E is robustly more informative than E' if the value of the decision maker's problem after observing E is always at least as high as the value of the decision maker's problem after observing E'. This comparison, which is strictly weaker than Blackwell's classical order, holds if and only if the null space of E is contained in the null space of E'.

econ.TH↗

arXiv2025

Data-Driven Persuasion

This paper develops a data-driven approach to Bayesian persuasion. The receiver is privately informed about the prior distribution of the state of the world, the sender knows the receiver's preferences but does not know the distribution of the state variable, and the sender's payoffs depend on the receiver's action but not on the state. Prior to interacting with the receiver, the sender observes the distribution of actions taken by a population of decision makers who share the receiver's preferences in best response to an unobserved distribution of messages generated by an unknown and potentially heterogeneous signal. The sender views any prior that rationalizes this data as plausible and seeks a signal that maximizes her worst-case payoff against the set of all such distributions. We show positively that the two-state many-action problem has a saddle point and negatively that the two-action many-state problem does not. In the former case, we identify adversarial priors and optimal signals. In the latter, we characterize the set of robustly optimal Blackwell experiments.

econ.TH↗