didunit: a unit-level multi-period differences-in-differences estimator in R

This R package estimates multi-period differences-in-differences at the level of the treated unit. This allows more flexible aggregation over estimators whose most granular differences-in-differences estimate is at the treated time and is useful in applications where there is considerable heterogeneity within the treated time group or when units treated at the same time receive somewhat different treatments. For example, when units are treated with different doses, they can be aggregated on the basis of dose to derive a dose-response function. Regional heterogeneity, as illustrated by a cross-country study on democratization, is another example. The software’s calls have the same syntax as the did package and agree with those estimates when panels are balanced and covariates are not relevant.

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Publication Details

Journal
SoftwareX
Published
2025-12-17
DOI
https://doi.org/10.1016/j.softx.2025.102467
Citations
1
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
2.63
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article

didunit: a unit-level multi-period differences-in-differences estimator in R

Ransi Clark, Jonathan N. Katz, R. Michael Alvarez
1 citations
SoftwareX
Statistical Methods in Clinical Trials
2.63
article

didunit: a unit-level multi-period differences-in-differences estimator in R

Ransi Clark, Jonathan N. Katz, R. Michael Alvarez
article en
1 citations

Abstract

This R package estimates multi-period differences-in-differences at the level of the treated unit. This allows more flexible aggregation over estimators whose most granular differences-in-differences estimate is at the treated time and is useful in applications where there is considerable heterogeneity within the treated time group or when units treated at the same time receive somewhat different treatments. For example, when units are treated with different doses, they can be aggregated on the basis of dose to derive a dose-response function. Regional heterogeneity, as illustrated by a cross-country study on democratization, is another example. The software’s calls have the same syntax as the did package and agree with those estimates when panels are balanced and covariates are not relevant.

SoftwareXVol. 33
California Institute of Technology (US)
Openalex Percentile: Top 7%
Statistical Methods in Clinical Trials
2.63
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