Triple Changes Estimator for Targeted Policies

Sina Akbari, Negar Kiyavash
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:666-695, 2024.

Abstract

The renowned difference-in-differences (DiD) estimator relies on the assumption of ’parallel trends,’ which may not hold in many practical applications. To address this issue, economists are increasingly considering the triple difference estimator as a more credible alternative. Both DiD and triple difference are limited to assessing average effects exclusively. An alternative avenue is offered by the changes-in-changes (CiC) estimator, which provides an estimate of the entire counterfactual distribution by relying on assumptions imposed on the distribution of potential outcomes. In this work, we extend the triple difference estimator to accommodate the CiC framework, presenting the ‘triple changes estimator’ and its identification assumptions, thereby expanding the scope of the CiC paradigm. Subsequently, we empirically evaluate the proposed framework and apply it to a study examining the impact of Medicaid expansion on children’s preventive care.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-akbari24a, title = {Triple Changes Estimator for Targeted Policies}, author = {Akbari, Sina and Kiyavash, Negar}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {666--695}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/akbari24a/akbari24a.pdf}, url = {https://proceedings.mlr.press/v235/akbari24a.html}, abstract = {The renowned difference-in-differences (DiD) estimator relies on the assumption of ’parallel trends,’ which may not hold in many practical applications. To address this issue, economists are increasingly considering the triple difference estimator as a more credible alternative. Both DiD and triple difference are limited to assessing average effects exclusively. An alternative avenue is offered by the changes-in-changes (CiC) estimator, which provides an estimate of the entire counterfactual distribution by relying on assumptions imposed on the distribution of potential outcomes. In this work, we extend the triple difference estimator to accommodate the CiC framework, presenting the ‘triple changes estimator’ and its identification assumptions, thereby expanding the scope of the CiC paradigm. Subsequently, we empirically evaluate the proposed framework and apply it to a study examining the impact of Medicaid expansion on children’s preventive care.} }
Endnote
%0 Conference Paper %T Triple Changes Estimator for Targeted Policies %A Sina Akbari %A Negar Kiyavash %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-akbari24a %I PMLR %P 666--695 %U https://proceedings.mlr.press/v235/akbari24a.html %V 235 %X The renowned difference-in-differences (DiD) estimator relies on the assumption of ’parallel trends,’ which may not hold in many practical applications. To address this issue, economists are increasingly considering the triple difference estimator as a more credible alternative. Both DiD and triple difference are limited to assessing average effects exclusively. An alternative avenue is offered by the changes-in-changes (CiC) estimator, which provides an estimate of the entire counterfactual distribution by relying on assumptions imposed on the distribution of potential outcomes. In this work, we extend the triple difference estimator to accommodate the CiC framework, presenting the ‘triple changes estimator’ and its identification assumptions, thereby expanding the scope of the CiC paradigm. Subsequently, we empirically evaluate the proposed framework and apply it to a study examining the impact of Medicaid expansion on children’s preventive care.
APA
Akbari, S. & Kiyavash, N.. (2024). Triple Changes Estimator for Targeted Policies. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:666-695 Available from https://proceedings.mlr.press/v235/akbari24a.html.

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