Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”

Giuseppe De Luca, Franco Peracchi, Jan R. Magnus

Risultato della ricerca: Comment/debate

Abstract

We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.
Lingua originaleEnglish
pagine (da-a)217-222
Numero di pagine6
RivistaJOURNAL OF BUSINESS & ECONOMIC STATISTICS
Volume37
Stato di pubblicazionePublished - 2019

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stability theory
Causal Effect
Misspecification
Stability Theory
Restriction
Unknown
Coefficient
evidence
Model
Evidence
Framework
Coefficients

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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abstract = "We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.",
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AU - Peracchi, Franco

AU - Magnus, Jan R.

PY - 2019

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N2 - We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.

AB - We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.

UR - http://hdl.handle.net/10447/354894

UR - http://pubs.amstat.org/loi/jbes

M3 - Comment/debate

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JO - JOURNAL OF BUSINESS & ECONOMIC STATISTICS

JF - JOURNAL OF BUSINESS & ECONOMIC STATISTICS

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