Equivalence and Synthesis of Causal Models

By Thomas Verma, Judea Pearl. 1990

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, background): “Achieving this based on observational data alone is challenging since, under the faithfulness assumption, the true DAG is only identifiable up to a Markov equivalence class [46].”
An Analysis of the Adaptation Speed of Causal Models (2020, background): “With data coming from a generic directed acyclic graph (DAG), we can only hope to discover the Markov equivalence class of this DAG (Verma and Pearl, 1991).”
Learning Optimal Fair Policies (2018, background): “Proof: The latent projection (Verma and Pearl 1990) of any K stage DAG onto X,S,M,A, Y suffices to identify and estimate the two path-specific effects in question, and this latent projection is the DAG in Fig.”
Causal Models for Dynamical Systems (2020, background): “…and on causal discovery [e.g. Spirtes et al., 2000, Pearl, 2009, Shpitser, 2018, Richardson and Spirtes, 2002, Hern´an and Robins, 2006, Evans, 2015, Richardson et al., 2017, Hyttinen et al., 2012, Verma and Pearl, 1991, Silva et al., 2006], more work is needed in the case of dynamical systems.”
On the Identifiability of the Post-Nonlinear Causal Model (2009, methodology): “Traditionally, in the implementation of most conditional independencebased causal discovery algorithms, such as PC (Spirtes et al., 2001) and IC (Verma & Pearl, 1990), it is assumed that the variables are either discrete or Gaussian with linear causal relations.”
Causal Discovery from Changes (2001, background): “[Verma and Pearl, 1990] T. Verma and J. Pearl.”
Learning Bayesian networks: The combination of knowledge and statistical data (1994, background): “Another assumption is that the user may be uncertain as to which belief-network structure is generating the data.”
Fantastic Generalization Measures and Where to Find Them (2019, methodology): “Our approach is inspired by the seminal work on Inductive Causation (IC) Algorithm by Verma and Pearl (1991), which provides a framework for learning a graphical model through conditional independence test.”
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011, background): “…equivalence classes of directed graphical models has a long tradition, perhaps starting with the criterion for two DAGs being Markov equivalent by Verma and Pearl (1990) and culminating in the graph theoretic characterization of essential graphs (also called CPDAGs , “completed partially…”
Causal interpretation of stochastic differential equations (2013, methodology): “For the DAG-based framwork, in the case of independent errors, parts of the causal structure may be learned from the observational distribution, as seen in [36], and intervention distributions may be calculated by a truncated factorization formula as in (3.”
Causal Inference on Discrete Data Using Additive Noise Models (2009, background): “Those constraint-based approaches are unable to distinguish among causal DAGs that impose the same independences (Markov equivalence classes, Verma and Pearl [21]).”
Caveats for Causal Reasoning with Equilibrium Models (2001, methodology): “1 Constraint-Based Learning Constraint-Based (CB) learning methods [Verma and Pearl, 1991; Spirtes and Glymour, 1991; Pearl and Verma, 1991; Spirtes et al., 2000; Cheng et al., 2002] (also known as “conditional independence search” methods) build a CI model by systematically checking the data for co”
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (2018, background): “It follows that f ( I ) must also satisfy the Markov property based on d-separation with respect to G (Verma & Pearl, 1991).”
Improving Markov Chain Monte Carlo Model Search for Data Mining (2004, background): “Equivalent DAGs are characterized as those with the same undirected skeleton and the same immoralities (Verma and Pearl, 1990).”
UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “Latent projections (Verma and Pearl, 1991) provide a formal way to obtain a true causal graph for marginalization.”