  In the 1990s, Joshua Angrist and Guido Imbens studied the causal
interpretation of Instrumental Variable estimates (a widespread methodology in
economics) through the lens of potential outcomes (a classical framework to
formalize causality in statistics). Bridging a gap between those two strands of
literature, they stress the importance of treatment effect heterogeneity and
show that, under defendable assumptions in various applications, this method
recovers an average causal effect for a specific subpopulation of individuals
whose treatment is affected by the instrument. They were awarded the Nobel
Prize primarily for this Local Average Treatment Effect (LATE). The first part
of this article presents that methodological contribution in-depth: the
origination in earlier applied articles, the different identification results
and extensions, and related debates on the relevance of LATEs for public policy
decisions. The second part reviews the main contributions of the authors beyond
the LATE. J. Angrist has pursued the search for informative and varied
empirical research designs in several fields, particularly in education. G.
Imbens has complemented the toolbox for treatment effect estimation in many
ways, notably through propensity score reweighting, matching, and, more
recently, adapting machine learning procedures.
