Professor Guido Imbens

Consider two heterogenous populations of agents who, when matched, jointly produce an output, Y. For example, teachers and classrooms of students together produce achievement, parents raise children, whose life outcomes vary in adulthood, assembly plant managers and workers produce a certain number of cars per month, and lieutenants and their platoons vary in unit effectiveness. Let W ∈ 𝕨= {ω1, . . . ,ωJ} and X ∈ 𝕩 = {x1, . . . ,xK} denote agent types in the two populations. Consider the following matching mechanism: take a random draw from the W = wj subgroup of the first population and match her with an independent random draw from the X = xk subgroup of the second population. Let β ;(wj, xk), the average match function (AMF), denote the expected output associated with this match. We show that (i) the AMF is identified when matching is conditionally exogenous, (ii) conditionally exogenous matching is compatible with a pairwise stable aggregate matching equilibrium under specific informational assumptions, and (iii) we calculate the AMF’s semiparametric efficiency bound.

We investigate the choice of the bandwidth for the regression discontinuity estimator.

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.

We show that these two types of interval estimate are different in practice, the latter in general being shorter.

In the last decade a growing body of research has studied inference on partially identified parameters (e.g., Manski, 1990, 2003). Here the authors introduce conceptually different interval estimates that asymptotically cover each element in the identification region with fixed probability (but not necessarily every element simultaneously).