Counterfactual Probabilities: Computational Methods, Bounds and Applications
It addresses the challenge of inferring causal effects from observational data, which is crucial for fields like medicine and law, but is incremental as it builds on existing causal frameworks.
The paper tackles the problem of evaluating counterfactual probabilities, such as in fault diagnosis and liability determination, by developing computational methods and bounds when only observational data is available, and demonstrates applications in treatment efficacy and product-safety litigation.
Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in product-safety litigation.