# Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics

arXiv: https://arxiv.org/abs/2301.06300 · HTML: https://mlsanity.com/paper/2301.06300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information](https://mlsanity.com/paper/1709.01447/) (2017)

## Abstract (arXiv)

According to the Lancet report on the global burden of disease published in October 2020, air pollution is among the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships within and between two multivariate time series data streams derived from wearable sensors: personal exposure to airborne particulate matter of aerodynamic sizes less than 2.5um (PM2.5) gathered from the Airspeck monitor worn on the person and continuous respiratory rate (breaths per minute) measured by the Respeck monitor worn as a plaster on the chest. Results are presented for a cohort of 113 asthmatic adolescents using the PCMCI+ algorithm to learn the short-term causal relationships between lags of ± exposure and respiratory rate. We consider causal effects up to a maximum delay of 8 hours, using data at both a 1 minute and 15 minute resolution in different experiments. For the first time a personalised exposure-response relationship between PM2.5 exposure and respiratory rate has been demonstrated to exist for short-term effects in asthmatic adolescents during their everyday lives. Our results lead to recommendations for work on specific open problems in causal discovery, to increase the feasibility of this approach for similar epidemiology studies in the future.