  The concept of Quality of Life (QoL) refers to a holistic measurement of an
individual's well-being, incorporating psychological and social aspects.
Pregnant women, especially those with obesity and stress, often experience
lower QoL. Physical activity (PA) has shown the potential to enhance the QoL.
However, pregnant women who are overweight and obese rarely meet the
recommended level of PA. Studies have investigated the relationship between PA
and QoL during pregnancy using correlation-based approaches. These methods aim
to discover spurious correlations between variables rather than causal
relationships. Besides, the existing methods mainly rely on physical activity
parameters and neglect the use of different factors such as maternal (medical)
history and context data, leading to biased estimates. Furthermore, the
estimations lack an understanding of mediators and counterfactual scenarios
that might affect them. In this paper, we investigate the causal relationship
between being physically active (treatment variable) and the QoL (outcome)
during pregnancy and postpartum. To estimate the causal effect, we develop a
Causal Machine Learning method, integrating causal discovery and causal
inference components. The data for our investigation is derived from a
long-term wearable-based health monitoring study focusing on overweight and
obese pregnant women. The machine learning (meta-learner) estimation technique
is used to estimate the causal effect. Our result shows that performing
adequate physical activity during pregnancy and postpartum improves the QoL by
units of 7.3 and 3.4 on average in physical health and psychological domains,
respectively. In the final step, four refutation analysis techniques are
employed to validate our estimation.
