The Direction of Time

By O. Penrose. 1962

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In Philosophy

Citations in the corpus

Toward Causal Representation Learning (2021, background): “Reichenbach [198] clearly articulated the connection between causality and statistical dependence.”
Towards Causal Representation Learning (2021, background): “Reichenbach [198] clearly articulated the connection between causality and statistical dependence.”
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, background): “tatistical dependence between two variables X and Y must have a causal explanation in that either (i) X causes Y (denoted X !Y), (ii) Y causes X, or (iii) X and Y have a common cause Z, i.e., X Z !Y (Reichenbach, 1956).2 The common cause principle indicates that different causal models can explain t”
Causality for Machine Learning (2019, background): “ystem, taking us outside the IID setting. We no longer work with the observational distribution, but a distribution where certain variables or mechanisms have changed. This is the realm of causality. Reichenbach (1956) clearly articulated the connection between causality and statistical dependence. ”
The Arrow of Time in Multivariate Time Series (2016, background): “This question is mainly academic but has received a lot of attention in literature, especially in physics, see Reichenbach (1956) or Price (1996).”
Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference (2015, background): “X → Z ← Y (common effect) naturally emerges from the time direction of appropriate mixing processes [35].”
Causal Markov condition for submodular information measures (2010, background): “It can be justiﬁed by the assumption that every dependence between them requires a common cause (as postulated by Reichenbach [3]), which should then explicitly appear in the causal model.”
Detecting low-complexity unobserved causes (2011, background): “Statistical dependences between two variables X and Y indicate that (A) X causes Y , (B) Y causes X, or (C) there is a third variable W influencing both X and Y [Reichenbach, 1956].”
Causal Inference Using the Algorithmic Markov Condition (2008, methodology): “The statistical version of this principle is part of Reichenbach’s principle of the common cause [32] stating that statistical dependences between random variables4 and are always due to at least one of the following three types of causal links: 1) is a cause of , or 2) vice versa, or 3) there is a ”
A theory of causal learning in children: causal maps and Bayes nets. (2004, background): “…correlation We can translate these informal intuitions about conditional dependence into the more precise language of probability theory (see Reichenbach, 1956 More formally, we could say that if Graph 1 is right and there is a causal chain that goes from parties to wine to insomnia, then Y…”
Seeing the Arrow of Time (2014, background): “There has been much study of how the underlying physics, and the second law of thermodynamics, create a direction of time [14, 16].”
Scientific Explanation (2005, background): “Since that time, philosophers have come to see claims about causal relations as having a rich empirical content that goes far beyond mere regularities and their instantiation (see Spirtes et al. (2000), though the tradition began well before Hempel made his remark, with Reichenbach (1956)).”
Estimating Causal Direction and Confounding of Two Discrete Variables (2016, background): “Further, assume that any probabilistic dependence between variables always arises due to a causal connection between the variables (Reichenbach, 1991).”
EPISTEMIC DEMOCRACY WITH DEFENSIBLE PREMISES1 (2013, background): “…which is usually understood thus: a more frequent than expected coincidence between a set of phenomena, which do not affect each other, is due to (possibly hidden) common causes, and the phenomena become independent once we conditionalize on these common causes (Reichenbach 1956: 159-160).”
Nonstationary Time Series, Cointegration, and the Principle of the Common Cause (2003, background): “Association is not, in general, correlation (as is clearly understood by both Reichenbach [1956], p.”