## 4 Discussion  
The ATE-Uplift framework effectively predicts causation in health outcomes when fitted using ULR. Uplift modeling acts as a cross-validation technique of the ATE estimation, integrating its causal inference and machine learning features. Moreover, Uplift modeling ensures the goodness-of-fit of the regression. Thus, categorical factual/counterfactuals tables generated from the original data are input to obtain ATEs, with positive values revealing causation. Then, Uplift modeling is employed, where positive Qini coefficients confirm causation.  
For the data considered by [PERSON] et al. (2016), their corresponding ULR fitting and distribution of benzene exposure ([PERSON] et al., 2016), our framework was useful in revealing the causation between indoor exposure to benzene and the risk of developing childhood AML. In contrast, no causation was revealed when considering outdoor exposure. Nevertheless, it was observed that, for this scenario, low ATE values (<0.05) near zero are somehow related to inconsistent Uplift modeling results, as the corresponding Qini coefficients also show low values (<0.03), different than zero. In these instances, we recommend, using a third cross-validation method when reporting low ATE values.  
While our preference is to have the original data to perform exploratory data analysis (EDA), evaluate the interaction between predictors, and consequently preselect -and later compare- applicable ML methods for estimating causation, in this work, we cautiously tested ATE-Uplift for generated data, with one predictor, benzene, as ATE and Uplift modeling are complementary methods for binary outcomes, working with factual and counterfactuals to consequently estimating causality. ML methods, although promising, must be carefully supported by a detailed EDA and a sample size evaluation. We noticed, for instance, that inconsistent Uplift modeling results are obtained when decreasing the sample size, an effect that must be minimized as per most ML predictors. Our future work will add to our framework an EDA that will allow us to observe trends among and within data groups, potentially fit and compare different surrogate models to establish the 'best' relationship between variables, and ultimately, perform a meta-analysis and comparison between causation-based ML methods. Some techniques to be explored in future works include meta-analysis and machine learning tools such as meta-learning causal structures and causal Bayesian networks.  
It is important to note that the reliability of the results obtained through the application of our ATE-Uplift framework depends largely on the completeness of the data used and how well-defined the assumptions are. Thus, once the numerical and categorical data is available, it is recommended to perform data analysis to verify or not the main assumptions regarding the absence of definers in the population required by the ATE model and, therefore, frame the limitations of the model extrapolation.  
Finally, we have assumed that there is no interaction between the benzene exposure and the rest of the predictors. Such interaction will be considered in future works adapting our framework once the data is available. ORs might be readjusted, and correlations between pollutants might be revisited for more accurate correlations representing the interaction within the strata predictors and benzene exposure.  
Our research's scientific and practical novelty lies simply and effectively in estimating the causation of health outcomes using two levels of confirmation or cross-validation. We use ATE values and Uplift modeling, integrating causal inference and machine learning features. Causation is, per se, the main goal when evaluating health outcomes since confirmed relationships between variables might not indicate that the outcome is indeed the result of the occurrence of the other event(s). A reliable causation approach may lead to effective disease prevention, monitoring, and treatment.