## 1 Introduction
### Current Trends in Estimating Causation for Health Outcomes Using Machine Learning Methods  
Causal analysis can be sometimes inaccurate, as in complex systems, it is difficult to make causal arguments based on single correlation coefficients. To overcome this challenge, recent machine learning (ML) approaches have been developed, based on causal pattern recognition, providing robustness in accurately modeling counterfactual scenarios [31]. ML methods have been applied to health sciences for causal inference [32]. For counterfactual prediction, ML has been used to address causal questions using methods such as Random Forest [33] and Bayesian additive regression trees (BART) [34]. Some common research goals that can be tackled with these techniques are [30] (i) the evaluation of potential causes of health outcomes, (ii) the assessment of treatment options, and (iii) the assessment of bias in the statistical analysis. Some ML predictors proved to be assertive when causally inferring the influence of a health outcome while being controlled by confounders. One study, for instance, showed that the targeted maximum likelihood estimation (TMLE) technique outperformed traditional models when analyzing the effect of fruit density on the nutrition of pregnant women on birth outcomes [35]. Despite being a promising alternative to causal analysis, it is still in the foreseeable future when we will be able to identify high-level causal variables from low-level data using these techniques, as causal modeling approaches such as meta-learning and meta-modeling will be able to find causal relationships accurately. Moreover, the potential case of being exposed to complex mixtures of chemical contaminants causing adverse health outcomes can have additive or synergistic effects, posing a challenge where the strength of the ML approach could be used in combination with existing human data to infer causality [36]. Uplift modeling has been used by several companies to estimate the effect of an action on some customer outcomes [37]. Estimating customer uplift is a causal inference since it requires determining the difference between two outcomes that are mutually exclusive for an individual (counterfactual nature); this is carried out using randomized experiments for the treatment group and the control group. At the same time, the Uplift estimation is also a machine learning problem because different models must be trained to finally select the one that yields the most reliable prediction, requiring a sensible cross-validation process [38]. The combination of these features taken from both approaches, causal inference, and machine learning, make Uplift modeling a suitable alternative in determining causation. To our knowledge, Uplift modeling has not been applied to health sciences. Uplift modeling might become unstable when predicting causation, as with any other ML technique, as the sample size decreases. Moreover, its convergence might be affected since it can depend on variables not typically used in response models. In this work, we propose a novel framework, for evaluating potential causes of health outcomes using the average treatment effect (ATE) to compare the effects in our computational experiments and Uplift modeling, as a machine learning technique employed for cross-validation of the ATEs. This simple approach effectively estimates the causation of health outcomes using two levels of confirmation, integrating causal inference and machine learning features. Our case study includes the causation of benzene exposure (as an air pollutant) to childhood AML, analyzing the counterfactual nature of the OR-based relationships found indoors and outdoors. The case study selected for our framework was presented by [PERSON] et al. (2016) and includes childhood AML data collected from California birth records over 17 years. Health outcomes based solely on relationships between variables can be challenged when the outcome is not proven to result from the occurrence of the other event (s).