Latent Variable Modeling and Applications to Causality

By Maia Berkane. 1997

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “Causal inference for longitudinal studies has been studied extensively, too [e.g. Vanderweele, 2015, Aalen et al., 2008, Robins, 1997], where the results are often formulated in the language of potential outcomes [Imbens and Rubin, 2015], rather than SCMs.”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, background): “The fact that the control function approach allows for more general dependencies among the unobservables and the conditioning variables than the matching approach allows is implicitly recognized in the work of Rosenbaum (1995) and Robins (1997).”
Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps (2012, background): “One is dynamic matching as in Robins (1997), which employs a dynamic version of unconfoundedness that allows treatment assignment at a given point in time to depend on past observable outcomes and covariates.2 The other approach (Heckman & Navarro, 2007), which is based on discrete-time dynamic…”
Graphical models for marked point processes based on local independence (2007, background): “For the discrete time case Robins (1986, 1997) has demonstrated that nevertheless in a situation like Fig.”
Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings (2014, unknown): “These assumptions link the potential outcome and the observed data (Robins, 1994, 1997).”
An experimental design for the development of adaptive treatment strategies (2005, background): “First we make the consistency assumption [11] that Sk+1 from the observable data is the same as the potential outcome Sk+1( ak) evaluated at ak = Ak , and so on, including S2 = S2(A1).”
Marginal Structural Models versus Structural nested Models as Tools for Causal inference (2000, methodology): “…g is very hard to estimate given a STM. Specifically, given a SNDM, we estimate the law of Y g as follows: (i) obtain an estimate β by g-estimation (Robins, 1997), (ii) estimate F • R(β0) (u) by the empirical law of the • R i β for i = 1, . . . , n, (iii) specify and estimate a parametric model…”
Association, Causation, And Marginal Structural Models (1999, background): “Robins (1997, 1999), Robins et al.”
Structural Equation Modeling For Travel Behavior Research (2001, background): “2 For discussions of SEM in the context of causal modeling see Berkane (1997), Pearl (2000), Shipley (2000), and Sprites, Glymour and Scheines (2001).”
Optimal Structural Nested Models for Optimal Sequential Decisions (2004, methodology): “Remark: Robins (1994,1997) proved Theorem 3.1 and then studied estimation of the parameters ψ † for ( ¯ d, ¯ d * ) -double-regime additive SNMMs in the special cases in which d and d * are (i) the same treatment regime and (ii) this common regime d is the identically " zero " regime-that is d m ( ¯…”