Bridging Methodologies: Angrist and Imbens’ Contributions to Causal Identification

Faire dialoguer économistes et statisticiens: les contributions d’Angrist et d’Imbens à l’identification causale

Lucas Girard and Yannick Guyonvarch
Additional contact information
Lucas Girard: CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique
Yannick Guyonvarch: UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

Post-Print from HAL

Abstract: 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.

Keywords: returns to schooling; US educational system; propensity score matching; causal machine learning; instrumental variables (IV); Neyman-Rubin causal model; local average treatment; effect (LATE); natural experiments; variables instrumentales (IV); modèle causal de Neyman-Rubin; effet local moyen du traitement (LATE); expériences naturelles; rendements de l’éducation; système éducatif états-unien; matching sur le score de propension; apprentissage statistique causal (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Published in Revue d'économie politique, 2023, 133 (6), pp.845-905. ⟨10.3917/redp.336.0845⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04431344

DOI: 10.3917/redp.336.0845

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().