Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
This addresses the need for trustworthy causal inference in health and sensor data analysis, though it is incremental as it builds on matching methods for distributional data.
The paper tackles the problem of analyzing causal effects on complex outcomes from wearable and sensor data by developing ADD MALTS, an interpretable method for distributional data analysis that outperforms existing methods in simulations and is applied to study continuous glucose monitors for diabetes risk mitigation.
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 of 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.