Mostly Harmless Econometrics: An Empiricist's Companion

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Machine Learning Methods That Economists Should Know About (2019, background): “1 Goals The traditional approach in econometrics, as exemplified in leading texts such as Wooldridge [2010], Angrist and Pischke [2008], Greene [2000] is to specify a target, an estimand, that is a functional of a joint distribution of the data. Often the target is a parameter of a statistical model”
Causal Effect Inference with Deep Latent-Variable Models (2017, neutral): “An extremely common practice in these cases is to rely on so-called “proxy variables” [38, 6, 36, Ch. 11].”
Deconfounding Reinforcement Learning in Observational Settings (2018, background): “A common practice is then to leverage observed proxy variables that contain information about the unobserved confounders (Angrist and Pischke, 2008; Maddala and Lahiri, 1992; Montgomery et al., 2000; Schölkopf et al., 2016).”
Openness and income: The roles of trade and migration (2014, methodology): “In the case of one endogenous regressor, as here, it coincides with the Angrist and Pischke (2009) test. 238 F. Ortega, G. Peri / Journal of International Economics 92 (2014) 231–251”
How does learner-centered education affect teacher self-efficacy? The case of project-based learning in Korea (2019, methodology): “For technical details, refer to Angrist and Pischke (2008).”