As a clinician treating patients CATE (conditional average treatment effect) is not adequate. What is needed is conditional treatment effect distribution (C-TED). We need to know what the risk of bad outcomes is, for each treatment option, including varying the timing, dose and protocol. We need to know if the tails of the C-TED can be anticipated, detected, and mitigated. For this reason we don't want to compute average outcomes, we need to propagate distributions through the model, from the observed variables, through the hidden (latent) variables to the outcomes. Knowing the shape of the distribution is critically important. In pathophysiology and therapeutics the causal effects may often be non-linear.
As a clinician treating patients CATE (conditional average treatment effect) is not adequate. What is needed is conditional treatment effect distribution (C-TED). We need to know what the risk of bad outcomes is, for each treatment option, including varying the timing, dose and protocol. We need to know if the tails of the C-TED can be anticipated, detected, and mitigated. For this reason we don't want to compute average outcomes, we need to propagate distributions through the model, from the observed variables, through the hidden (latent) variables to the outcomes. Knowing the shape of the distribution is critically important. In pathophysiology and therapeutics the causal effects may often be non-linear.