Recursive versus non-recursive systems: An attempt at a synthesis

By R. H. Strotz, H. Wold. 2017

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

External Validity: From Do-Calculus to Transportability Across Populations (2014, methodology): “…1975; Davis, 1984; Verma and Pearl, 1988; Spirtes, Glymour and Scheines, 1993; Pearl, 1995) together with models of interventions (Haavelmo, 1943; Strotz and Wold, 1960) and counterfactuals (Neyman, 1923; Rubin, 1974; Robins, 1986; Balke and Pearl, 1995) provides such a language and renders the…”
Causal inference in statistics: An overview (2009, methodology): “Such an extension, based on simulating hypothetical interventions in the model, was proposed in (Haavelmo, 1943; Strotz and Wold, 1960; Spirtes et al., 1993; Pearl, 1993a, 2000a; Lindley, 2002) and has led to new ways of defining and estimating causal effects in nonlinear and nonparametric models (t”
Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification (1999, methodology): “An explicit translation of interventions into “wiping out” equations from the model was first proposed by Strotz and Wold (1960) and later used in Fisher (1970), Sobel (1990), Spirtes et al. (1993), and Pearl (1995).”
Identification and Likelihood Inference for Recursive Linear Models with Correlated Errors (2006, unknown): “Both interpretations echo themes in earlier work by Haavelmo [24], Strotz and Wold [61], Box [7].”
TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “The statistician, Nanny Wermuth (1992), argued that since ax = E(Y”
3. The Foundations of Causal Inference (2010, methodology): “Using the do(x) notation enables us to correctly express the notion of “holding Z fixed” and to obtain a simple definition of the controlled direct effect of the transition from X = x to X = x′: CDE = E(Y | do(x′), do(z)) − E(Y | do(x), do(z)).”
Causal inference (1991, background): “The difference between P(B) and P(B||A) can depend both upon the value of B and the value of A. Manipulations in SEMs are described in Strotz and Wold (1960), Spirtes et al. (2001), Pearl (2001), and Lauritzen (2001).”
Causal and Counterfactual Inference (2018, methodology): “Guided by ideas from structural econometrics (Haavelmo, 1943; Strotz and Wold, 1960; Spirtes et al., 1993), I have explored a conditioning operator called do(x) (Pearl, 1995) that captures the intent of Px(y) by simulating an intervention in a causal model of interdependent variables (Pearl, 2009b).”
On Pearl’s Hierarchy and the Foundations of Causal Inference (2022, methodology): “It was then made more explicit and called “wiping out” by Strotz and Wold [1960].”
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models (2009, methodology): “Following §6 of Lauritzen and Richardson (2002), we can consider a Markov chain Monte Carlo method for simulating from a distribution represented with inter- the target of intervention with a simple assignment operator (Strotz and Wold, 1960).”
Nested Markov Properties for Acyclic Directed Mixed Graphs (2012, methodology): “…a causally interpreted DAG, the consequences of intervention in the system under study can be understood by modifying the graph via removing certain edges, and modifying the corresponding joint probability distribution via re-weighting (Pearl, 2000; Spirtes et al., 1993; Strotz and Wold, 1960).”
A Lewisian Logic of Causal Counterfactuals (2013, unknown): “We can also model the situation where both 6 This approach to modeling interventions originated in econometrics (Strotz and Wold 1960; Fisher 1970), and was masterfully articulated and developed by Pearl (2009). interventions take place by MX=0, Z=1, which has the following structural equations: X…”