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

arXiv:2312.10569v2 Announce Type: replace
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 …

abstract analysis arxiv causal causal inference cs.lg current data devices eess.sp example inference mean modern questions sensor sensors statistics stat.me summarizing type wearable wearable devices