Ensemble Methods for Causal Effects in Panel Data Settings

By Susan Athey, Mohsen Bayati, Guido W. Imbens, Zhaonan Qu. 2019

48 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, methodology): “This approach is analyzed in Bickel et al. [1998] and Van der Vaart [2000] for the general semiparametric case, and in Chernozhukov et al. [2017] for the average treatment effect case.”
The GDP-Temperature relationship: Implications for climate change damages (2021, unknown): “3For example, see Friedman (1953); Dhrymes et al. (1972); Cooley and LeRoy (1981); Leamer (1978, 1983); White (1996); Yatchew (1998); Hansen et al. (2011), and Belloni et al. (2014). 4See Keynes (1939), Koopmans (1947), Leamer (1978), Leamer (1983), Hendry et al.”