Lucas Girard, Yannick Guyonvarch
arXiv 20 Feb 2024 · Econometrics · publishedRevue d économie politique (2024) · 2 citations (OpenAlex)
arXiv:2402.13023 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Imbens, G. and J. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 8 | 4 | 100% |
| 2 | Angrist, J. and G. Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity | 1.000 | 7 | 4 | 100% |
| 3 | Angrist, J., G. Imbens, and D. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 1.000 | 7 | 4 | 100% |
| 4 | Imbens, G (2010) Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) | 1.000 | 6 | 4 | 100% |
| 5 | Angrist, J (1990) Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records | 0.928 | 4 | 3 | 100% |
| 6 | Angrist, J. and J.-S. Pischke (2010) The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics | 0.874 | 5 | 2 | 100% |
| 7 | Angrist, J. and M. Rokkanen (2015) Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff | 0.874 | 5 | 2 | 100% |
| 8 | Imbens, G (1992) An Efficient Method of Moments Estimator for Discrete Choice Models With Choice-Based Sampling | 0.874 | 5 | 2 | 100% |
| 9 | Angrist, J., K. Graddy, and G. Imbens (2000) The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for F… | 0.811 | 4 | 2 | 100% |
| 10 | Rubin, D (1974) Estimating causal effects of treatments in randomized and nonrandomized studies | 0.737 | 3 | 3 | 67% |
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