Stratified Analysis of `Probabilities of Causation'
This work offers incremental improvements for public policy assessment and decision-making by refining causal inference bounds.
The paper tackles the problem of bounding probabilities of causation by deriving narrower bounds than existing Tian-Pearl bounds using covariate information from experimental and observational data, and provides identifiable cases under no-prevention assumptions.
This paper proposes new formulas for the probabilities of causation difined by Pearl (2000). Tian and Pearl (2000a, 2000b) showed how to bound the quantities of the probabilities of causation from experimental and observational data, under the minimal assumptions about the data-generating process. We derive narrower bounds than Tian-Pearl bounds by making use of the covariate information measured in experimental and observational studies. In addition, we provide identifiable case under no-prevention assumption and discuss the covariate selection problem from the viewpoint of estimation accuracy. These results are helpful in providing more evidence for public policy assessment and dicision making problems.