Ensemble Methods for Causal Effects in Panel Data Settings

Susan Athey, Mohsen Bayati, Guido Imbens and Zhaonan Qu

Papers from arXiv.org

Abstract: This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.

Date: 2019-03
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Citations: View citations in EconPapers (27)

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http://arxiv.org/pdf/1903.10079 Latest version (application/pdf)

Related works:
Journal Article: Ensemble Methods for Causal Effects in Panel Data Settings (2019)
Working Paper: Ensemble Methods for Causal Effects in Panel Data Settings (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1903.10079

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