Data-driven model selection within the matrix completion method for causal panel data models
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https://doi.org/10.7910/DVN/JGGBQG
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Replication data for application
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2024-02-05
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Data-driven model selection within the matrix completion method for causal panel data models
Replication data for application
DataONE2024-02-05 更新80
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models*
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the
DataCite Commons2024-09-20 更新90
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models
This article develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if th
DataCite Commons2025-06-01 更新130
Matrix Completion Methods for Causal Panel Data Models
In this paper we study methods for estimating causal effects in settings with panel data, where a subset of units are exposed to a treatment during a subset of periods, and the goal is estimating coun
NBER2018-10-01 更新10
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models
This article develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if th
DataCite Commons2024-09-20 更新180
