Inequality Constraints in Causal Models with Hidden Variables

  title={Inequality Constraints in Causal Models with Hidden Variables},
  author={Changsung Kang and Jin Tian},
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. 
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Publications referenced by this paper.
Showing 1-10 of 18 references

Graphical models for causal inference

  • S. Lauritzen
  • In O.E. Barndorff-Nielsen,
  • 2000
Highly Influential
3 Excerpts

Wasserman . Estimation of effects of sequential treatments by reparameterizing directed acyclic graphs

  • James M. Robins, A. Larry
  • 2000

pages 116–125

  • G. F. Cooper, C. Yoo. Causal discovery from a mixture of experimental, observational data. InProc. of UAI
  • San Francisco, CA,
  • 1999

pages 226–235

  • Dan Geiger, Christopher Meek. Quantifier elimination for statistical probl UAI
  • San Francisco, CA,
  • 1999

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