Estimating Outcome Distributions for Compliers in Instrumental Variables Models

By Guido W. Imbens, Donald B. Rubin. 1997

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The Econometrics of Randomized Experiments (2016, background): “This section builds on the discussions in Angrist (2004), Hirano, Imbens, Rubin, and Zhou (2000), Imbens and Rubin (1997ab) and Bertanha and Imbens (2014).”
Regression discontinuity design with many thresholds (2017, unknown): “Imbens and Rubin (1997)) and define three types of compliance groups.”
Testing exclusion restrictions and additive separability in sample selection models (2014, background): “Equivalent to Imbens and Rubin (1997) in the context of treatment endogeneity, it follows that the former is a mixture of the always selected and the compliers, where the mixing proportions correspond to the relative shares of types in the conditional outcome distribution. f (y|Z = 1, S = 1) = πa…”
Nonparametric estimation and inference about the overlap of two distributions (2012, background): “) Choose M,Mφ, v, vφ > 0 and Borel sets B0, Bφ such that B(M) ⊂ Cf,g, B(Mφ) ⊂ Cφ, (26) B0 ⊂ B(M, v), Bφ ⊂ B(Mφ, vφ) (27) ∫ ∫”
Using Instrumental Variables for Inference About Policy Relevant Treatment Effects (2018, unknown): “The testable implications of these assumptions for the IV model are well-known; see, for example, Balke and Pearl (1997), Imbens and Rubin (1997), and Kitagawa (2015).”
SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE (2009, background): “Other work focusing on treatment effect heterogeneity includes Heckman and Robb (1985), Manski (1990), Imbens and Rubin (1997), Lalonde (1995), Dehejia (1997), Heckman and Smith (1993), Heckman, Smith, and Clements (1997), Lechner (1999), and Abadie, Angrist, and Imbens (2002).”
Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men (2011, background): “The never-takers are those who will not move 32 LATE was introduced by Imbens and Angrist (1994) and further developed by Angrist and Imbens (1995), Angrist, Imbens, and Rubin (1996), Imbens and Rubin (1997), Heckman and Vytlacil (2001) and Imbens (2001), among others.”
The Causal Effect of Class Size on Academic Achievement (2011, methodology): “Using the ML approach, we estimate the causal effects more efficiently than can the 2SLS approach (Bollen, 1996; Imbens & Rubin, 1997a, 1997b; Little & Yau, 1998).”
Principal Stratification (2015, methodology): “With these additional restrictions, Equation 1 reduces to the following: ITT ¼ pcITTc þ pa ITTa |ffl{zffl} 0 þ pn ITTn |ffl{zffl} 0 þ pd |{z} 0 ITTd ¼ pcITTc: ð2Þ Next, we estimate both ITT and pc directly from the data.”
Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, methodology): “[57–60] develop approaches to estimating the CACE that use the mixture structure to improve efficiency.”
Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures (2016, background): “For that goal, one may look at the entire cumulative distribution functions (c.d.f.) or all quantiles, as Imbens and Rubin (1997), Abadie (2002), Abadie, Angrist and Imbens (2002), Firpo (2007) and Frölich and Melly (2007).”