Automating Path Analysis for Building Causal Models from Data

Abstract

Path analysis is a generalization of multiple linear regression that builds models with causal interpretations. It is an exploratory or discovery procedure for finding causal structure in correlational data. Recently, we have applied statistical methods such as path analysis to the problem of building models of AI programs, which are generally complex and poorly understood. For example, we built by hand a path-analytic causal model of the behavior of the Phoenix planner. Path analysis has a huge search space, however. If one measures N parameters of a system, then one can build O(2N2) causal models relating these parameters. For this reason, we have developed an algorithm that heuristically searches the space of causal models. This paper describes path analysis and the algorithm, and presents preliminary empirical results, including what we believe is the first example of a causal model of an AI system induced from performance data by another AI system.