Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:3340-3348, 2024.

Abstract

Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be misleading. For example, disparate distributions can have the same means, variances, and other statistics. Researchers can overcome the loss in information by instead representing the data as distributions. We develop an interpretable method for distributional data analysis that ensures trustworthy and robust decision making: Analyzing Distributional Data via Matching After Learning to Stretch (ADD MALTS). We (i) provide analytical guarantees of the correctness of our estimation strategy, (ii) demonstrate via simulation that ADD MALTS outperforms other distributional data analysis methods at estimating treatment effects, and (iii) illustrate ADD MALTS’ ability to verify whether there is enough cohesion between treatment and control units within subpopulations to trustworthily estimate treatment effects. We demonstrate ADD MALTS’ utility by studying the effectiveness of continuous glucose monitors in mitigating diabetes risks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-katta24a, title = {Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data}, author = {Katta, Srikar and Parikh, Harsh and Rudin, Cynthia and Volfovsky, Alexander}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {3340--3348}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/katta24a/katta24a.pdf}, url = {https://proceedings.mlr.press/v238/katta24a.html}, abstract = {Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be misleading. For example, disparate distributions can have the same means, variances, and other statistics. Researchers can overcome the loss in information by instead representing the data as distributions. We develop an interpretable method for distributional data analysis that ensures trustworthy and robust decision making: Analyzing Distributional Data via Matching After Learning to Stretch (ADD MALTS). We (i) provide analytical guarantees of the correctness of our estimation strategy, (ii) demonstrate via simulation that ADD MALTS outperforms other distributional data analysis methods at estimating treatment effects, and (iii) illustrate ADD MALTS’ ability to verify whether there is enough cohesion between treatment and control units within subpopulations to trustworthily estimate treatment effects. We demonstrate ADD MALTS’ utility by studying the effectiveness of continuous glucose monitors in mitigating diabetes risks.} }
Endnote
%0 Conference Paper %T Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data %A Srikar Katta %A Harsh Parikh %A Cynthia Rudin %A Alexander Volfovsky %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-katta24a %I PMLR %P 3340--3348 %U https://proceedings.mlr.press/v238/katta24a.html %V 238 %X Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be misleading. For example, disparate distributions can have the same means, variances, and other statistics. Researchers can overcome the loss in information by instead representing the data as distributions. We develop an interpretable method for distributional data analysis that ensures trustworthy and robust decision making: Analyzing Distributional Data via Matching After Learning to Stretch (ADD MALTS). We (i) provide analytical guarantees of the correctness of our estimation strategy, (ii) demonstrate via simulation that ADD MALTS outperforms other distributional data analysis methods at estimating treatment effects, and (iii) illustrate ADD MALTS’ ability to verify whether there is enough cohesion between treatment and control units within subpopulations to trustworthily estimate treatment effects. We demonstrate ADD MALTS’ utility by studying the effectiveness of continuous glucose monitors in mitigating diabetes risks.
APA
Katta, S., Parikh, H., Rudin, C. & Volfovsky, A.. (2024). Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:3340-3348 Available from https://proceedings.mlr.press/v238/katta24a.html.

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