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Source: Econometrics Journal, The
Resulting in 2 citations.
1. Ban, Kyunghoon
Kedagni, Desire
Nonparametric Bounds on Treatment Effects with Imperfect Instruments
Econometrics Journal published online (29 November 2021): DOI: 10.1093/ectj/utab033.
Also: https://academic.oup.com/ectj/advance-article/doi/10.1093/ectj/utab033/6445996
Cohort(s): Young Men
Publisher: Royal Economic Society (RES)
Keyword(s): Educational Returns; Modeling; Modeling, Nonparametric Regression; Research Methodology

Permission to reprint the abstract has not been received from the publisher.

This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling.
Bibliography Citation
Ban, Kyunghoon and Desire Kedagni. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments." Econometrics Journal published online (29 November 2021): DOI: 10.1093/ectj/utab033.
2. Farbmacher, Helmut
Huber, Martin
Laffers, Lukas
Langen, Henrika
Spindler, Martin
Causal Mediation Analysis with Double Machine Learning
Econometrics Journal published online (31 January 2022): DOI: 10.1093/ectj/utac003/6517682.
Also: https://academic.oup.com/ectj/advance-article/doi/10.1093/ectj/utac003/6517682
Cohort(s): NLSY97
Publisher: Royal Economic Society (RES)
Keyword(s): Health Care; Health/Health Status/SF-12 Scale; Insurance, Health; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust w.r.t. misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting. We demonstrate that the effect estimators are asymptotically normal and n−1/2-consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the U.S. National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect.
Bibliography Citation
Farbmacher, Helmut, Martin Huber, Lukas Laffers, Henrika Langen and Martin Spindler. "Causal Mediation Analysis with Double Machine Learning." Econometrics Journal published online (31 January 2022): DOI: 10.1093/ectj/utac003/6517682.