The probability approach in econometrics

By T. Haavelmo. 1944

Subfields: Statistics and Probability, Economics & Operations Research

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “The notion of invariant, autonomous, and independent mechanisms has appeared in various guises throughout the history of causality research [72], [100], [111], [124], [183], [188], [240].”
Towards Causal Representation Learning (2021, background): “Early work on this was done by Haavelmo [99], stating the assumption that changing one of the structural assignments leaves the other ones invariant.”
Causal Models for Dynamical Systems (2020, neutral): “Different causal methodology and several extensions of SCMs have been proposed [Wiener, 1956, Granger, 1969, Schreiber, 2000, White and Lu, 2010, Hyttinen et al., 2013, Peters et al., 2013, Pfister et al., 2018b], mostly considering discrete time models such as vector autoregressive models [Lütkepoh”
Causality for Machine Learning (2019, background): “Independence of mechanisms can be defined as vanishing mutual algorithmic 7Early work on this was done by Haavelmo (1944), stating the assumption that changing one of the structural assignments leaves the other ones invariant.”
Invariant Risk Minimization (2019, background): “We can observe this in Example 1 where, using two training environments, robust learning yields predictors that work well for σ ∈ [10, 20], while IRM yields predictors that work well for all σ.”
Conditional variance penalties and domain shift robustness (2017, background): “Causal models thus 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; Schölkopf et al.”
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): “Such a weaker requirement is sometimes referred to as ‘modularity’ (Pearl, 2009) or what is called ‘autonomy’ (Haavelmo, 1944; Aldrich, 1989); structural equations are autonomous if, whenever we replace one of them because of an intervention, no other structural equations change; they remain invaria”
Causal Structure Learning (2017, background): “This is also referred to as autonomy (Frisch, 1938; Haavelmo, 1944).”
Anchor regression: Heterogeneous data meet causality (2018, background): “Both of these results are direct implications of well-known invariance properties of causal models [Haavelmo, 1944, Aldrich, 1989, Pearl, 2009].”
Specification tests for partially identified models defined by moment inequalities (2013, background): “The discussion of the importance of a correct specification for inference purposes dates back to Haavelmo (1944). moment inequality is binding, diverges to infinity at an appropriate rate, c.f. Andrews and Soares (2010).”
Modelling skewness and kurtosis with the BCPE density in GAMLSS (2012, result): “Therefore, methodologically, the work presented here is consistent with the work of Haavelmo [9,10] and Mandelbrot [13] in the sense of a distribution that brings a degree of order and understanding at different time periods and/or scales of time.”
The Blessings of Multiple Causes (2018, background): “This idea of invariance has also been discussed in Haavelmo (1944), Aldrich (1989), Lanes (1988), Pearl (2009), Schölkopf et al. (2012), and Dawid and Didelez (2010) under the terms “autonomy,” “mod-ularity,” and “stability.”