Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
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Summary
The settable systems framework is offered as an extension of the Pearl Causal Model that permits causal discourse in systems embodying optimization, equilibrium, and learning and may prove generally useful for machine learning.
- Type
- article
- Published
- 2009-12-01
- Cited by
- 68
- References
- 44
- OpenAlex
- https://openalex.org/W1957172765
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7968761
Keywords
Pearl, Computer science, Artificial intelligence, Curse of dimensionality, Extension (predicate logic)
References
- Parallel Asynchronous Global Search and the Nested Optimization Scheme
- Total Least Squares and Errors-in-Variables Modeling : Analysis, Algorithms and Applications
- Decision-Theoretic Foundations for Causal Reasoning
- Game theory for applied economists
- Cause and Correlation in Biology
- An Extended Class of Instrumental Variables for the Estimation of Causal Effects
- Statistics and Causal Inference
- Constrained supervised learning
- Book Review of Causality: Models, Reasoning, and Inference
- A Stochastic Approximation Method
- Multi-Agent Influence Diagrams for Representing and Solving Games
- A Convergence Result for Learning in Recurrent Neural Networks
- Identifying independence in bayesian networks
- A Correspondence Principle for Simultaneous Equation Models
- A Learning Algorithm for Boltzmann Machines
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Time-series estimation of the effects of natural experiments
- Theory and practice of recursive identification
- Equilibrium Points in N-Person Games.
- Nonparametric Adaptive Learning with Feedback
Cited by
- Four essays in econometrics
- A Distinction between Causal Effects in Structural and Rubin Causal Models
- Identification of Local Treatment Effects Using a Proxy for an Instrument
- An Extended Class of Instrumental Variables for the Estimation of Causal Effects
- A FLEXIBLE NONPARAMETRIC TEST FOR CONDITIONAL INDEPENDENCE
- External Validity: From Do-Calculus to Transportability Across Populations
- Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection
- Causality, prediction, and specification analysis: Recent advances and future directions
- Causal diagrams for empirical legal research: a methodology for identifying causation, avoiding bias and interpreting results
- Causal Discourse in a Game of Incomplete Information
- Identification and Identification Failure for Treatment Effects Using Structural Systems
- Granger Causality, Exogeneity, Cointegration, and Economic Policy Analysis
- The Role of Causal Beliefs in Technology-Supported Policy
- Regression and Causation: A Critical Examination of Six Econometrics Textbooks
- Causality, Conditional Independence, and Graphical Separation in Settable Systems
- Algorithms of causal inference for the analysis of effective connectivity among brain regions
- Effective connectivity: Influence, causality and biophysical modeling
- Granger Causality and Dynamic Structural Systems
- GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA
- Bayesian networks and boundedly rational expectations
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