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AAAI 2021

Bounding Causal Effects on Continuous Outcome

Conference Paper AAAI Technical Track on Reasoning under Uncertainty Artificial Intelligence

Abstract

We 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 the high-dimensional context.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
838100637903847050
v2026.09.13