mlsanityData to 7 Oct 2026

Quantifier Elimination for Statistical Problems

arXiv 1301.6698 ↗First posted 23 Jan 2013cs.AI cs.LOcited by 8

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Abstract (arXiv)

Recent improvement on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the following problems completely automatically: 1. listing all equality and inequality constraints implied by a graphical model with hidden variables. 2. Comparing graphyical models with hidden variables (i.e., model equivalence, inclusion, and overlap). 3. Answering questions about the identification of a model or portion of a model, and about bounds on quantities derived from a model. 4. Determing whether a given set of independence assertions. We discuss the foundation of quantifier elimination and demonstrate its application to these problems.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2013.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 3.

8 papers.

  1. 2021An Automated Approach to Causal Inference in Discrete Settingscited by 39
  2. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  3. 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learningcited by 8
  4. 2023Scalable Computation of Causal Boundscited by 5
  5. 2021Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variablescited by 3
  6. 2021Restricted Hidden Cardinality Constraints in Causal Modelscited by 1
  7. 2024Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independenciescited by 1
  8. 2024A Counterfactual Analysis of the Dishonest Casinocited by 0