Replication data for: Ensemble Methods for Causal Effects in Panel Data Settings

Principal Investigator(s): View help for Principal Investigator(s) Susan Athey; Mohsen Bayati; Guido Imbens; Zhaonan Qu

Version: View help for Version V1

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data			12/07/2019 03:58:PM
LICENSE.txt	text/plain	14.6 KB	12/07/2019 10:58:AM

Project Citation:

Athey, Susan, Bayati, Mohsen, Imbens, Guido, and Qu, Zhaonan. Replication data for: Ensemble Methods for Causal Effects in Panel Data Settings. Nashville, TN: American Economic Association [publisher], 2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-07. https://doi.org/10.3886/E116483V1

Project Description

Summary: In many prediction problems researchers have found that combinations of prediction methods ("ensembles") perform better than individual methods. In this paper we apply these ideas to synthetic control type problems in panel data. Here a number of conceptually quite different methods have been developed. We compare the predictive accuracy of three methods with an ensemble method and find that the latter dominates. These results show that ensemble methods are a practical and effective method for the type of data configurations typically encountered in empirical work in economics, and that these methods deserve more attention.

Scope of Project

JEL Classification:
C23 Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
C33 Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models

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Published Versions

V1 [2019-12-07]

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