Data-driven model selection within the matrix completion method for causal panel data models

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Replication data for application

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2024-03-06

Data-driven model selection within the matrix completion method for causal panel data models

Replication data for application

NIAID Data Ecosystem00

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

Taylor & Francis Group2024-09-20 更新30

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

Taylor & Francis Group2024-09-20 更新20

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 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