Designing Transportable Experiments Under S-admissability

My Phan, David Arbour, Drew Dimmery, Anup Rao
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2539-2547, 2021.

Abstract

We consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE) on a target population. We propose a novel approach which explicitly considers the target when designing the experiment on the source. Under the covariate shift assumption, we design an unbiased importance-weighted estimator for the target population’s ATE. To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population. We show that Target Balance achieves a higher variance reduction asymptotically than methods that do not consider the target population during the design phase. Our experiments illustrate that Target Balance reduces the variance even for small sample sizes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-phan21a, title = { Designing Transportable Experiments Under S-admissability }, author = {Phan, My and Arbour, David and Dimmery, Drew and Rao, Anup}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2539--2547}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/phan21a/phan21a.pdf}, url = {https://proceedings.mlr.press/v130/phan21a.html}, abstract = { We consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE) on a target population. We propose a novel approach which explicitly considers the target when designing the experiment on the source. Under the covariate shift assumption, we design an unbiased importance-weighted estimator for the target population’s ATE. To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population. We show that Target Balance achieves a higher variance reduction asymptotically than methods that do not consider the target population during the design phase. Our experiments illustrate that Target Balance reduces the variance even for small sample sizes. } }
Endnote
%0 Conference Paper %T Designing Transportable Experiments Under S-admissability %A My Phan %A David Arbour %A Drew Dimmery %A Anup Rao %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-phan21a %I PMLR %P 2539--2547 %U https://proceedings.mlr.press/v130/phan21a.html %V 130 %X We consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE) on a target population. We propose a novel approach which explicitly considers the target when designing the experiment on the source. Under the covariate shift assumption, we design an unbiased importance-weighted estimator for the target population’s ATE. To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population. We show that Target Balance achieves a higher variance reduction asymptotically than methods that do not consider the target population during the design phase. Our experiments illustrate that Target Balance reduces the variance even for small sample sizes.
APA
Phan, M., Arbour, D., Dimmery, D. & Rao, A.. (2021). Designing Transportable Experiments Under S-admissability . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2539-2547 Available from https://proceedings.mlr.press/v130/phan21a.html.

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