Estimation of Bounds on Potential Outcomes For Decision Making

Maggie Makar, Fredrik Johansson, John Guttag, David Sontag
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6661-6671, 2020.

Abstract

Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess risks and benefits. We show that, in such cases, we can improve sample efficiency by estimating simple functions that bound these outcomes instead of estimating their conditional expectations, which may be complex and hard to estimate. Our analysis highlights a trade-off between the complexity of the learning task and the confidence with which the learned bounds hold. Guided by these findings, we develop an algorithm for learning upper and lower bounds on potential outcomes which optimize an objective function defined by the decision maker, subject to the probability that bounds are violated being small. Using a clinical dataset and a well-known causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-makar20a, title = {Estimation of Bounds on Potential Outcomes For Decision Making}, author = {Makar, Maggie and Johansson, Fredrik and Guttag, John and Sontag, David}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6661--6671}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/makar20a/makar20a.pdf}, url = {https://proceedings.mlr.press/v119/makar20a.html}, abstract = {Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess risks and benefits. We show that, in such cases, we can improve sample efficiency by estimating simple functions that bound these outcomes instead of estimating their conditional expectations, which may be complex and hard to estimate. Our analysis highlights a trade-off between the complexity of the learning task and the confidence with which the learned bounds hold. Guided by these findings, we develop an algorithm for learning upper and lower bounds on potential outcomes which optimize an objective function defined by the decision maker, subject to the probability that bounds are violated being small. Using a clinical dataset and a well-known causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.} }
Endnote
%0 Conference Paper %T Estimation of Bounds on Potential Outcomes For Decision Making %A Maggie Makar %A Fredrik Johansson %A John Guttag %A David Sontag %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-makar20a %I PMLR %P 6661--6671 %U https://proceedings.mlr.press/v119/makar20a.html %V 119 %X Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess risks and benefits. We show that, in such cases, we can improve sample efficiency by estimating simple functions that bound these outcomes instead of estimating their conditional expectations, which may be complex and hard to estimate. Our analysis highlights a trade-off between the complexity of the learning task and the confidence with which the learned bounds hold. Guided by these findings, we develop an algorithm for learning upper and lower bounds on potential outcomes which optimize an objective function defined by the decision maker, subject to the probability that bounds are violated being small. Using a clinical dataset and a well-known causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.
APA
Makar, M., Johansson, F., Guttag, J. & Sontag, D.. (2020). Estimation of Bounds on Potential Outcomes For Decision Making. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:6661-6671 Available from https://proceedings.mlr.press/v119/makar20a.html.

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