Causal Intervention

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5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

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Design of Experiment for Discovering Directed Mixed Graph
Sep 2, 2025Haijie Xu, Chen ZhangGraph Structure Learning Causal Discovery
Department of Industrial Engineering Tsinghua University
Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty
Jul 30, 2025Jeroen F. Uleman, Loes Crielaard, Leonie K. Elsenburg +4Dynamical Systems Causal Intervention
Copenhagen Health Complexity Center, University of Copenhagen, Copenhagen, Denmark. · Department of Public and Occupational Health, Amsterdam UMC University of Amsterdam, Amsterdam, The Netherlands. · Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands. +4
Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift
Apr 3, 2025Kwangho KimDistribution Shift Robustness Semiparametric Inference
Department of Statistics, Korea University
The Minimal Search Space for Conditional Causal Bandits
Feb 10, 2025Francisco N. F. Q. Simoes, Itai Feigenbaum, Mehdi Dastani +1Multi-Armed Bandits Causal Intervention
Department of Computer Science, Delft University of Technology, The Netherlands · Lehman College and The Graduate Center, City University of New York, New York · Department of Information and Computing Sciences, Utrecht University, The Netherlands

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Design of Experiment for Discovering Directed Mixed Graph

Haijie Xu, Chen Zhang

Department of Industrial Engineering Tsinghua University

Graph Structure Learning Causal Discovery Causal Intervention Causal Discovery with Latent Confounding

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We study the design of interventions for causal discovery in simple structural causal models whose causal graphs are directed mixed graphs (DMGs) that may contain directed cycles and bidirected edges representing latent confounding. In such case, observational conditional-independence (CI) information may not identify even the graph skeleton, while CI alone cannot generally detect a bidirected edge coexisting with a directed edge. To this end, we propose a stage-wise framework based on tailored separating systems. Separating-system interventions first recover descendant relations and strongly connected components (SCCs). The SCCs are then ordered by ancestry, and an SCC-Anc separating system recovers the directed subgraph. Given this subgraph, further systems use CI tests interpreted through

d

- or

σ

-separation to recover non-adjacent bidirected edges, whose endpoints share no directed edge, and do-see comparisons to recover those coexisting with exactly one directed edge. Under our assumptions, the framework recovers the directed subgraph and every bidirected edge except double-adjacent ones, whose endpoints are connected by a directed edge in each direction. We develop algorithms for unrestricted and

M

-bounded settings, with each experiment targeting at most

M

variables in the latter. For recovering the directed subgraph and non-adjacent bidirected edges, our upper bounds on the number and maximum size of experiments match corresponding worst-case lower bounds up to logarithmic factors.

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Related topics

Latest papers 64

Design of Experiment for Discovering Directed Mixed Graph

Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty

Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift

The Minimal Search Space for Conditional Causal Bandits