Bayesian Model Averaging for Data Driven Decision Making when Causality
  is Partially Known
  Probabilistic machine learning models are often insufficient to help with
decisions on interventions because those models find correlations - not causal
relationships. If observational data is only available and experimentation are
infeasible, the correct approach to study the impact of an intervention is to
invoke Pearl's causality framework. Even that framework assumes that the
underlying causal graph is known, which is seldom the case in practice. When
the causal structure is not known, one may use out-of-the-box algorithms to
find causal dependencies from observational data. However, there exists no
method that also accounts for the decision-maker's prior knowledge when
developing the causal structure either. The objective of this paper is to
develop rational approaches for making decisions from observational data in the
presence of causal graph uncertainty and prior knowledge from the
decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to
infer set of causal graphs that can represent the data generation process. We
provide decisions by computing the expected value and risk of potential
interventions explicitly. We demonstrate our approach by applying them in
different example contexts.
