Autonomy

By Daniel Thalmann. 2005

634 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Philosophy

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Citations in the corpus

Toward Causal Representation Learning (2021, methodology): “Overviews are provided by Aldrich [4], Hoover [111], Pearl [183], and Peters et al. [188, Section 2.2].”
Towards Causal Representation Learning (2021, background): “Aldrich [4], Hoover [111], Pearl [183], and Peters et al.”
Causal Models for Dynamical Systems (2020, background): “Many researchers from various disciplines engaged in this question and developed the fundamental assumptions that are often referred to as invariance, autonomy or modularity [Wright, 1921, Haavelmo, 1944, Aldrich, 1989, Hoover, 1990, Imbens and Rubin, 2015, Richardson and Robins, 2013].”
Invariant Risk Minimization (2019, background): “The blue curve shows (3) as we vary the coeﬃcient c for a linear data representation Φ( x ) = x Diag([1 , c ]), and w = (1 , 0).”
Conditional variance penalties and domain shift robustness (2017, background): “…have the deﬁning advantage that the predictions will be valid even under arbitrarily large interventions on all predictor variables (Haavelmo, 1944; Aldrich, 1989; Pearl, 2009; Sch¨olkopf et al., 2012; Peters et al., 2016; Zhang et al., 2013, 2015; X. Yu, 2017; M. Rojas-Carulla, 2017; Magliacane…”
Invariant Causal Prediction for Nonlinear Models (2017, background): “This follows from an assumption sometimes called autonomy or modularity (Haavelmo, 1944; Aldrich, 1989; Hoover, 1990; Pearl, 2009; Sch¨olkopf et al., 2012).”
Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “The second condition is closely related to what is sometimes called “autonomy” [Aldrich, 1989] of structural equations [see also “stability” Pearl, 2009, e.g. Sec.”
Anchor regression: Heterogeneous data meet causality (2018, neutral): “Both of these results are direct implications of well-known invariance properties of causal models [Haavelmo, 1944, Aldrich, 1989, Pearl, 2009].”
Some varieties of robustness (2006, background): “This condition is obviously closely connected to the econometric notion of autonomy, in the sense discussed in Aldrich (1989).”
When Choice Motivates and When It Does Not (2007, background): “In this theory and its related studies, the need for autonomy is equated with the striving for self-realization and self-determination (Deci & Ryan, 2000; Lindley, 1986; Ryan, 1993).”
MODEL DISCOVERY AND TRYGVE HAAVELMO’S LEGACY (2014, background): “2, he distinguishes explicitly between the constancy of empirical relations and the autonomy of theoretical ones, initiated by Frisch (1938):1 see later contributions by Aldrich (1989) and Aldrich (1994). Although expectations, a major concern of many economists after Lucas (1976), only occur once i”
The Econometrics of Macroeconomic Modelling (2005, background): “Johansen 1977, and Aldrich 1989). Models with a high degree of autonomy represent structure: they remain invariant to changes in economic policies and other shocks to the economic system, as implied by the definition above. However, structure is partial in two respects: first, autonomy is a relative”