Counterfactual Reconciliation: Incorporating Aggregation Constraints for More Accurate Causal Effect Estimates

dc.authorscopusid 57205096068
dc.authorscopusid 39763212900
dc.contributor.author Cengiz, D.
dc.contributor.author Tekgüç, Hasan
dc.contributor.author Tekgüç, H.
dc.date.accessioned 2023-10-19T15:05:14Z
dc.date.available 2023-10-19T15:05:14Z
dc.date.issued 2022
dc.department-temp Cengiz, D., The Home Depot, United States; Tekgüç, H., Kadir Has University, Turkey en_US
dc.description.abstract We extend the scope of the forecast reconciliation literature and use its tools in the context of causal inference. Researchers are interested in both the average treatment effect on the treated and treatment effect heterogeneity. We show that ex post correction of the counterfactual estimates using the aggregation constraints that stem from the hierarchical or grouped structure of the data is likely to yield more accurate estimates. Building on the geometric interpretation of forecast reconciliation, we provide additional insights into the exact factors determining the size of the accuracy improvement due to the reconciliation. We experiment with U.S. GDP and employment data. We find that the reconciled treatment effect estimates tend to be closer to the truth than the original (base) counterfactual estimates even in cases where the aggregation constraints are non-linear. Consistent with our theoretical expectations, improvement is greater when machine learning methods are used. © 2022 International Institute of Forecasters en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.ijforecast.2022.08.011 en_US
dc.identifier.issn 0169-2070
dc.identifier.scopus 2-s2.0-85143872304 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.ijforecast.2022.08.011
dc.identifier.uri https://hdl.handle.net/20.500.12469/4752
dc.identifier.wosquality Q1
dc.khas 20231019-Scopus en_US
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation.ispartof International Journal of Forecasting en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 0
dc.subject Causal machine learning methods en_US
dc.subject Counterfactual estimation en_US
dc.subject Difference-in-differences en_US
dc.subject Forecast reconciliation en_US
dc.subject Non-linear constraints en_US
dc.title Counterfactual Reconciliation: Incorporating Aggregation Constraints for More Accurate Causal Effect Estimates en_US
dc.type Article en_US
dspace.entity.type Publication
relation.isAuthorOfPublication 65b30906-4ef5-49b1-a10e-f86c44eb7b0d
relation.isAuthorOfPublication.latestForDiscovery 65b30906-4ef5-49b1-a10e-f86c44eb7b0d

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