ABSTRACT
This paper deals with the problem of modeling counterfactual reasoning in scenarios where, apart from the observed endogenous variables, we have a latent variable that affects the outcomes and, consequently, the results of counterfactuals queries. This is a common setup in healthcare problems, including mental health. We propose a new framework where the aforementioned problem is modeled as a multivariate regression and the counterfactual model accounts for both observed and a latent variable, where the latter represents what we call the patient individuality factor ( φ ). In mental health, focusing on individuals is paramount, as past experiences can change how people see or deal with situations, but individuality cannot be directly measured. To the best of our knowledge, this is the first counterfactual approach that considers both observational and latent variables to provide deterministic answers to counterfactual queries, such as: what if I change the social support of a patient, to what extent can I change his/her anxiety? The framework combines concepts from deep representation learning and causal inference to infer the value of φ and capture both non-linear and multiplicative effects of causal variables. Experiments are performed with both synthetic and real-world datasets, where we predict how changes in people's actions may lead to different outcomes in terms of symptoms of mental illness and quality of life. Results show the model learns the individually factor with errors lower than 0.05 and answers counterfactual queries that are supported by the medical literature. The model has the potential to recommend small changes in people's lives that may completely change their relationship with mental illness.
ABSTRACT
PURPOSE: To investigate the existence of faster vs slower recovery profiles in futsal and factors distinguishing them. METHODS: 22 male futsal players were evaluated in countermovement jump, 10-m sprint, creatine kinase, total quality of recovery (TQR), and Brunel Mood Scale (fatigue and vigor) before and immediately and 3, 24, and 48 h posttraining. Hierarchical cluster analysis allocated players to different recovery profiles using the area under the curve (AUC) of the percentage differences from baseline. One-way ANOVA compared the time course of each variable and players' characteristics between clusters. RESULTS: Three clusters were identified and labeled faster recovery (FR), slower physiological recovery (SLphy), and slower perceptual recovery (SLperc). FR presented better AUC in 10-m sprint than SLphy (P = .001) and SLperc (P = .008), as well as better TQR SLphy (P = .018) and SLperc (P = .026). SLperc showed better AUC in countermovement jump than SLphy (P = .014) but presented worse fatigue AUC than SLphy (P = .014) and FR (P = .008). AUC of creatine kinase was worse in SLphy than in FR (P = .001) and SLperc (P < .001). The SLphy players were younger than SLperc players (P = .027), whereas FR were slower 10-m sprinters than SLphy players (P = .003) and SLperc (P = .013) and tended to have higher maximal oxygen consumption than SLphy (effect size =1.13). CONCLUSION: Different posttraining recovery profiles exist in futsal players, possibly influenced by their physical abilities and age/experience.
