Bounding Probabilities of Causation with Partial Causal Diagrams
This work addresses the challenge of making causal inferences in practical scenarios where full causal diagrams are unavailable, offering a method for decision-makers in fields like medicine or policy, though it is incremental as it builds on existing bounding techniques.
The paper tackles the problem of bounding probabilities of causation, which are crucial for individual-level decisions but often not fully identifiable from data, by proposing a framework that uses partial causal information to derive tighter bounds. It shows how to incorporate available structural or statistical constraints into an optimization formulation, extending applicability to realistic incomplete causal knowledge settings.
Probabilities of causation are fundamental to individual-level explanation and decision making, yet they are inherently counterfactual and not point-identifiable from data in general. Existing bounds either disregard available covariates, require complete causal graphs, or rely on restrictive binary settings, limiting their practical use. In real-world applications, causal information is often partial but nontrivial. This paper proposes a general framework for bounding probabilities of causation using partial causal information. We show how the available structural or statistical information can be systematically incorporated as constraints in a optimization programming formulation, yielding tighter and formally valid bounds without full identifiability. This approach extends the applicability of probabilities of causation to realistic settings where causal knowledge is incomplete but informative.