Instrumental variables, selection models, and tight bounds on the average treatment effect

By James J. Heckman, E. Vytlacil. 2000

139 citations recorded by the source corpus.

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In Statistics and Probability

Citations in the corpus

Instrumental variables and the sign of the average treatment effect (2019, unknown): “2 is the same monotonicity assumption found in Imbens and Angrist (1994), who also refer to it as an assumption of “no defiers.”
Partial Identification of Local Average Treatment Effects With an Invalid Instrument (2013, background): “A strand of this literature constructs bounds on treatment effects assuming the validity of the instrument (Manski 1990, 1994; Balke and Pearl 1997; Heckman and Vytlacil 1999, 2000; Bhattacharya, Shaikh, and Vytlacil 2008; Kitagawa 2009; Huber and Mellace 2010; Shaikh and Vytlacil 2011; Chen,…”
Partial Identification in Triangular Systems of Equations With Binary Dependent Variables (2011, methodology): “Proof: Follows from Theorem 2 of Heckman and Vytlacil (2001) .”
Inference in Incomplete Models (2006, unknown): “Remark 6: Equivalence of (i) and (iii) is a generalization of proposition 1 of Jovanovic (1989) to the case where P is not necessarily atomless and U not necessarily compact.”
Trimming for Bounds on Treatment Effects with Missing Outcomes (2002, background): “Others (Balke and Pearl 1997; Heckman and Vytlacil 1999, 2000a, 2000b) have constructed such bounds to address a different problem – that of imperfect compliance of the treatment, even when “intention” to treat is effectively randomized (Bloom 1984; Robins 1989; Imbens and Angrist, 1994; Angrist,…”
Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture (2001, methodology): “D x D D 0 The function gx(u) is defined in Heckman and Vytlacil (1999, 2000a).”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, methodology): “The local instrumental variables estimator (LIV) estimand introduced in Heckman (1997), and developed further in Heckman and Vytlacil (1999, 2000, 2005) and Florens et al. (2002), will allow us to recover a version of the marginal treatment effect (MTE) parameter.”
Towards causal benchmarking of bias in face analysis algorithms (2020, methodology): “If the data is generated from a perfectly randomized or controlled study, the quantity E j − E j is a good estimate of the “average treatment effect” (ATE) [5, 32, 57, 66] of covariate j on e, or the average change in e over all examples when covariate j is flipped from 0 to 1, with other covariates”