Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

TL;DR

This paper introduces a Bayesian Model Averaging approach to incorporate causal graph uncertainty and prior knowledge into decision-making from observational data, addressing the limitations of existing methods that assume known causal structures.

Contribution

It develops a rational framework using ensemble methods to account for causal graph uncertainty and prior knowledge in decision-making, extending Pearl's causality framework.

Findings

Effective integration of causal graph uncertainty into decision models.

Demonstrated improved decision quality in example contexts.

Framework accommodates prior knowledge and observational data.

Abstract

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.