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Three conditioning strategies are compared and it is demonstrated that using a causal approach to synthetic training data generation can lead to tangible benefits, with insights offering valuable guidance to machine learning practitioners for the effective design of data generation protocols.
This work proposes counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables and demonstrates this framework's utility for quantitative robustness evaluation.
Passive physical activity is increasingly used as a digital-phenotyping proxy for mood, assuming a stable relationship across people and time. We tested this assumption using daily mood and activity data from 1,072 individuals (121 women, 951 men; 13,909 person-days) in the Juli app. The within-person activity-mood ass...
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