A joint test of unconfoundedness and common trends

Martin Huber and Eva-Maria Oe{\ss}

Papers from arXiv.org

Abstract: This paper introduces an overidentification test of two alternative assumptions to identify the average treatment effect on the treated in a two-period panel data setting: unconfoundedness and common trends. Under the unconfoundedness assumption, treatment assignment and post-treatment outcomes are independent, conditional on control variables and pre-treatment outcomes, which motivates including pre-treatment outcomes in the set of controls. Conversely, under the common trends assumption, the trend and the treatment assignment are independent, conditional on control variables. This motivates employing a Difference-in-Differences (DiD) approach by comparing the differences between pre- and post-treatment outcomes of the treatment and control group. Given the non-nested nature of these assumptions and their often ambiguous plausibility in empirical settings, we propose a joint test using a doubly robust statistic that can be combined with machine learning to control for observed confounders in a data-driven manner. We discuss various causal models that imply the satisfaction of either common trends, unconfoundedness, or both assumptions jointly, and we investigate the finite sample properties of our test through a simulation study. Additionally, we apply the proposed method to five empirical examples using publicly available datasets and find the test to reject the null hypothesis in two cases.

Date: 2024-04, Revised 2024-06
New Economics Papers: this item is included in nep-big and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2404.16961 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2404.16961

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().