On the logic of causal inference.

By D. Weed. 1986

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In Causal Inference & Discovery

Citations in the corpus

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].”
Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “The procedure in S 1 , which will be further discussed in Section 4.1, contains the methods proposed in (Hoover, 1990; Tian and Pearl, 2001; Peters et al., 2016) for causal discovery from changes as special cases.”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “There exist methods for causal discovery from changes of multiple data sets [Hoover, 1990; Tian and Pearl, 2001; Peters et al. , 2016] by exploiting the property of invariance of causal mechanisms.”
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): “This invariance idea is closely linked to causality and has been discussed, for example, under the term ‘autonomy’ and ‘modularity’ (Haavelmo, 1944; Aldrich, 1989; Hoover, 1990; Pearl, 2009; Schölkopf et al., 2012) or also ‘stability’ (Dawid and Didelez (2010) and Pearl (2009), section 1.”
Causal Discovery from Changes (2001, background): “The economist Kevin Hoover (1990) attempted to in fer the direction of causal influences among economic variables (e.g., employment and money supply) by ob serving the changes that sudden modifications in the economy (e.g., tax reform, labor dispute) induced in the statistics of these variables.”
Causal Transfer in Machine Learning (2015, background): “If the input (which in this case coincides with the covariates) shifts, the mechanism should thus remain invariant [Hoover, 1990, Janzing and Sch¨olkopf, 2010, Peters et al., 2015].”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “The procedure in Case 1 contains the methods proposed in [20, 21] for causal discovery from changes as special cases, which may also be interpreted as special cases of the principle underlying the method for Case 2: if one of P ( cause ) and P ( effect | cause ) changes while the other remains…”
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019, background): “In many scientiﬁc ﬁelds, we are often faced with (Hoover, 1990; Tian & Pearl, 2001; Huang et al., 2015; Zhang et al., 2017; Huang et al., 2017; Peters et al., 2016; Ghassami et al., 2018).”
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.”
Invariant Models for Causal Transfer Learning (2015, background): “From the point of view of causal modeling (Pearl, 2009), assuming invariance of conditionals makes sense if the conditionals represent causal mechanisms (e.g., Hoover, 1990), see Section 2.3 for details.”