Inequality Constraints in Causal Models with Hidden Variables
This work addresses the challenge of causal inference in the presence of unmeasured variables, providing tools for model testing, but it is incremental as it builds on existing instrumental inequality methods.
The paper tackles the problem of bounding causal effects in models with hidden variables, deriving inequality constraints for causal Bayesian networks that apply to both observational and experimental data.
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.