Inequality Constraints in Causal Models with Hidden Variables

Abstract

We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.

Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)

References in corpus (5)

• Causal Discovery from a Mixture of Experimental and Observational Data
• On the Testability of Causal Models with Latent and Instrumental Variables
• Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
• Instrumentality Tests Revisited
• Graphical Models and Exponential Families