Identification Design

TL;DR

This paper introduces a model of identification design for robust causal inference, analyzing how information structures influence manipulability and identification in microeconometric environments.

Contribution

It develops a theoretical framework for understanding manipulability and provides conditions for exact identification and payoff bounds based on information disclosure.

Findings

All treatment-effects models are manipulable.

Manipulation is feasible with almost fully informative structures.

Rich covariate disclosure can eliminate manipulation in experiments.

Abstract

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.