Bounding Causal Effects on Continuous Outcome
Keywords:Causality, Graphical Models
AbstractWe investigate the problem of bounding causal effects from experimental studies in which treatment assignment is randomized but the subject compliance is imperfect. It is well known that under such conditions, the actual causal effects are not point-identifiable due to uncontrollable unobserved confounding. In their seminal work, Balke and Pearl (1994) derived the tightest bounds over the causal effects in this settings by employing an algebra program to derive analytic expressions. However, Pearl's approach assumes the primary outcome to be discrete and finite. Solving such a program could be intractable when high-dimensional context variables are present. In this paper, we present novel non-parametric methods to bound causal effects on the continuous outcome from studies with imperfect compliance. These bounds could be generalized to settings with a high-dimensional context.
How to Cite
Zhang, J., & Bareinboim, E. (2021). Bounding Causal Effects on Continuous Outcome. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12207-12215. https://doi.org/10.1609/aaai.v35i13.17449
AAAI Technical Track on Reasoning under Uncertainty