When studying an outcome Y that is weakly positive but can equal zero (eg, earnings),
researchers frequently estimate an average treatment effect (ATE) for a “log-like” …

Inferring causal effects of treatments is a central goal in many disciplines. The potential
outcomes framework is a main statistical approach to causal inference, in which a causal …

Given a set of baseline assumptions, a breakdown frontier is the boundary between the set
of assumptions which lead to a specific conclusion and those which do not. In a potential …

In the treatment effect problem, the available information is on the marginal distributions of
potential outcomes, but not on their joint distribution. The only point identified functional of …

Equivalence scales are routinely applied to adjust the income of households of different
sizes and compositions. Because of their practical importance for the measurement of …

This paper examines identification power of the instrument exogeneity assumption in the
treatment effect model. We derive the identification region: The set of potential outcome …

Many causal estimands are only partially identifiable since they depend on the
unobservable joint distribution between potential outcomes. Stratification on pretreatment …

This article proposes an identification and estimation method that allows researchers to
bound continuous functionals of the joint distribution of potential outcomes from the literature …

Abstract Machine learning is used to make decisions for individuals in various fields, which
require us to achieve good prediction accuracy while ensuring fairness with respect to …

In this paper, we develop asymptotically valid inference in moment equality models with
incomplete data, where the sample information is insufficient to identify the joint distribution …