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

TL;DR

This paper introduces ADD MALTS, an interpretable method for causal inference using distributional data from wearable sensors, providing analytical guarantees and outperforming existing methods in estimating treatment effects.

Contribution

The paper presents a novel distributional data analysis method, ADD MALTS, with theoretical guarantees and improved performance over existing approaches for causal inference.

Findings

ADD MALTS outperforms other methods in simulation studies.

It provides analytical guarantees for estimation correctness.

Demonstrates utility in analyzing glucose monitor effectiveness.

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 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.