Jung Hyub Lee | Searcharxiv

arXiv2026

Conformalized Lee Inference: Distribution-Free Prediction Sets for Individual Treatment Effect under Monotone Sample Selection

Treatment can change which outcomes are observed, so treated-selected and selected-control units need not represent the same latent population. This paper proposes conformalized Lee inference for counterfactual prediction under randomized treatment and monotone sample selection. The procedure uses treated-selected observations to fit and calibrate an arbitrary prediction rule and replaces the usual $(1-α)$ score quantile with the adjusted $(1-απ)$ quantile, where $π$ is the identified share of always-selected units among treated-selected units. The resulting prediction set has finite-sample, distribution-free marginal coverage over the sharp Lee ambiguity set. For a selected-control unit, subtracting the observed untreated outcome yields a marginal prediction set for the realized individual treatment effect. The adjusted population cutoff is minimax optimal over the reduced-information identification region. Simulations show that ordinary split-conformal prediction can under-cover under distribution shifts induced by selection, whereas the adjusted procedures restore coverage. Empirical analysis uses the National Job Corps Study data to illustrate prediction sets for individual wage effects of assignment to program access.

econ.EM↗

arXiv2026

Profiled Anderson--Rubin Test: Robust Inference Allowing for Direct Effects of Instruments

Instrumental variable analyses often rely on the assumption that instruments affect the outcome only through the endogenous regressor. In many applications, researchers can defend only a plausible range for direct effects of instruments, while conventional sensitivity analyses may be unreliable when instruments are weak. This paper proposes the profiled Anderson--Rubin (pAR) test, which considers all direct effects within a prespecified range and retains a candidate effect whenever at least one admissible direct effect is consistent with the data. Under the maintained sampling assumptions, the procedure controls false rejection for each compatible candidate without requiring strong instruments. The paper provides practical methods for constructing confidence sets and distinguishes substantive bounds from bounds tied to the realized instrument design. Simulations and applications to retirement saving and returns to schooling show that the procedure resembles conventional sensitivity analysis when instruments are strong but preserves substantially more uncertainty when identification is weak.

econ.EM↗