Instrumental Variables: An Econometrician's Perspective

By Guido W. Imbens. 2014

253 citations recorded by the source corpus.

I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrumental variables methods and in what settings these may be plausible. By providing context to the current applications a better understanding of the applicability of these methods may arise.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Invariant Causal Prediction for Nonlinear Models (2017, background): “The assumption of no direct eﬀect of E on Y is analogous to the typical assumptions about instrumental variables (Angrist et al., 1996; Imbens, 2014).”
Confidence intervals for causal effects with invalid instruments by using two‐stage hard thresholding with voting (2016, unknown): “Often, covariates, potentially many, are controlled for to make instruments more plausibly valid (Hernán and Robins, 2006; Swanson and Hernán, 2013; Baiocchi et al., 2014; Imbens, 2014).”
Lagged Explanatory Variables and the Estimation of Causal Effect (2017, background): “We note here that by substituting equations (18) and (20) into (19), we can express X solely in terms of model parameters and errors: Xit = αδ FEi + α 1it + δ FEi + ηit + φ 2t−1 1− αβ (22) This expression reveals the magnitude of endogeneity bias when regressing Yt on Xt.”
Latent class instrumental variables: A clinical and biostatistical perspective (2015, methodology): “For our review, we bring a clinical and biostatistical perspective and cover topics not covered or covered sparingly in other recent reviews [11, 13].”
Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments (2018, unknown): “See Imbens (2014) for a review, including discussion of both the potential outcomes framework and graphical models.”
The confidence interval method for selecting valid instrumental variables (2019, background): “For recent reviews and examples, see Clarke and Windmeijer (2012), Imbens (2014), Kang et al. (2016) and Burgess et al. (2017).”
Decomposing Treatment Effect Variation (2016, methodology): “(18) This approach is based onM-estimation, though there aremany other ways to formalize the TSLS estimator (e.g., Imbens 2014).”
Estimating peer effects in networks with peer encouragement designs (2016, background): “…designs, the randomized encouragement Z i , endogenous behavior D i , and outcome Y i are all defined as direct interventions on or measurements of the same individual, often making this assumption implausible because the encouragement may affect that individual in many ways (30, 36, 37).”