Estimating the average causal effect of intervention in continuous variables using machine learning

The causal effect is defined by Pearl's do operation as a probability distribution over observed data in the way that it is altered from one which generates data originally [Pearl 1995; Pearl 2009]. When dealing with causal effects in realworld problems, it is also necessary to take into account unobserved variables that is not included in data. In general, causal effects are counterfactual probability distributions that differ from data generating systems in the real world. When we consider the existence of unobserved data, it becomes a problem if it can be determined by observed data available. That is, we need to consider the identifiability of causal effects in this case. This problem has recently been resolved to a certain extent [Tian and Pearl 2002; Shpitser and Pearl 2006; Shpitser and Pearl 2012].