Data-driven exclusion criteria for instrumental variable studies

Tony Liu, Patrick Lawlor, Lyle Ungar, Konrad Kording
Proceedings of the First Conference on Causal Learning and Reasoning, PMLR 177:485-508, 2022.

Abstract

When using instrumental variables for causal inference, it is common practice to apply specific exclusion criteria to the data prior to estimation. This exclusion, critical for study design, is often done in an ad hoc manner, informed by a priori hypotheses and domain knowledge. In this study, we frame exclusion as a data-driven estimation problem, and apply flexible machine learning methods to estimate the probability of a unit complying with the instrument. We demonstrate how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates. We show the utility of our approach with a fuzzy regression discontinuity analysis of the effect of initial diabetes diagnosis on follow-up blood sugar levels. Data-driven exclusion criterion can help improve both power and external validity for various quasi-experimental settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v177-liu22a, title = {Data-driven exclusion criteria for instrumental variable studies}, author = {Liu, Tony and Lawlor, Patrick and Ungar, Lyle and Kording, Konrad}, booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning}, pages = {485--508}, year = {2022}, editor = {Schölkopf, Bernhard and Uhler, Caroline and Zhang, Kun}, volume = {177}, series = {Proceedings of Machine Learning Research}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v177/liu22a/liu22a.pdf}, url = {https://proceedings.mlr.press/v177/liu22a.html}, abstract = { When using instrumental variables for causal inference, it is common practice to apply specific exclusion criteria to the data prior to estimation. This exclusion, critical for study design, is often done in an ad hoc manner, informed by a priori hypotheses and domain knowledge. In this study, we frame exclusion as a data-driven estimation problem, and apply flexible machine learning methods to estimate the probability of a unit complying with the instrument. We demonstrate how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates. We show the utility of our approach with a fuzzy regression discontinuity analysis of the effect of initial diabetes diagnosis on follow-up blood sugar levels. Data-driven exclusion criterion can help improve both power and external validity for various quasi-experimental settings.} }
Endnote
%0 Conference Paper %T Data-driven exclusion criteria for instrumental variable studies %A Tony Liu %A Patrick Lawlor %A Lyle Ungar %A Konrad Kording %B Proceedings of the First Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2022 %E Bernhard Schölkopf %E Caroline Uhler %E Kun Zhang %F pmlr-v177-liu22a %I PMLR %P 485--508 %U https://proceedings.mlr.press/v177/liu22a.html %V 177 %X When using instrumental variables for causal inference, it is common practice to apply specific exclusion criteria to the data prior to estimation. This exclusion, critical for study design, is often done in an ad hoc manner, informed by a priori hypotheses and domain knowledge. In this study, we frame exclusion as a data-driven estimation problem, and apply flexible machine learning methods to estimate the probability of a unit complying with the instrument. We demonstrate how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates. We show the utility of our approach with a fuzzy regression discontinuity analysis of the effect of initial diabetes diagnosis on follow-up blood sugar levels. Data-driven exclusion criterion can help improve both power and external validity for various quasi-experimental settings.
APA
Liu, T., Lawlor, P., Ungar, L. & Kording, K.. (2022). Data-driven exclusion criteria for instrumental variable studies. Proceedings of the First Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 177:485-508 Available from https://proceedings.mlr.press/v177/liu22a.html.

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