Causality: Models

2000

Subfields: Causal Inference & Discovery

Citations in the corpus

• Discovering Causal Signals in Images (2016, background): “Such interventional semantics of what is meant by causation align with current approaches in the literature [25, 22].”
• A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (2016, positive): “, P(Y = y|do(X = x)) or simply P(y|do(x)), can be computed by the truncated factorization formula [Pearl, 2009]”
• Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020, methodology): “Given the SCM, accordingly we perform counterfactual reasoning by following Pearl’s procedure [12].”
• Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017, neutral): “Consequently, how to infer causal relations from observational data or combinations of observational and experimental data, known as causal discovery [9], [6], has drawn much attention in several disciplines in the past three decades.”
• Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, neutral): “Then we can use the standard orientation rules for unshielded triples to orient the edge between V k and V l [ Spirtes et al. , 2001; Pearl, 2000 ] .”
• Recursive partitioning for heterogeneous causal effects (2015, neutral): “[14], [15]) is focused on estimating the population (marginal) average treatment effect E[Yi(1)− Yi(0)].”
• Direct and Indirect Effects (2001, neutral): “Pearl (2000) used, interchangeably, Px(y), P(y|do(x)), P(y|x̂), and P(yx), and showed their equivalence to probabilities of subjunctive conditionals: P((X = x) □→ (Y = y)) (Lewis 1973).”
• Causal Discovery from Changes (2001, neutral): “Several such algorithms have been developed, including IC al­ gorithm [Pearl, 2000, section 2.5] (initially intro­ duced in [Pearl and Verma, 1991]) and PC algo­ rithm (Spirtes et al., 1993].”
• Ancestral Causal Inference (2016, unknown): “This approach conveniently applies to various types of interventions: perfect interventions [16], soft interventions [14], mechanism changes [24], and activity interventions [15].”
• Causal Generative Domain Adaptation Networks (2018, methodology): “The FCM (a tuple (cid:104) F , P (cid:105) ) consists of a set of equations F = ( F 1 , . . . , F D ) : and a probability distribution P over E = ( E 1 , . . . , E D ) .”
• Estimation of linear non-Gaussian acyclic models for latent factors (2009, unknown): “Many algorithms have been proposed for discovery of causal relations among observed variables [8,12].”
• Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings (2014, unknown): “The long-standing puzzle of ‘‘what causes what’’, formally known as the problem of causality inference, is challenging yet central in science [1,2].”
• Sequences of Mechanisms for Causal Reasoning in Artificial Intelligence (2013, unknown): “In AI this theory has been generalized by [Pearl(2000)] and others (e.”
• Identifiability of Path-Specific Effects (2005, unknown): “The ’forcing’ of the variables toz is called an intervention, and is denoted by do(z) in [Pearl, 2000].”
• A Survey on Causal Inference (2020, unknown): “For the structural causal model, it is suggested to refer the survey [91] or the book [90].”
• Detecting Confounding in Multivariate Linear Models via Spectral Analysis (2017, unknown): “Conditional statistical independences, which are usually employed for causal inference, [2, 3] are not able to distinguish between the above cases since there may not be conditional independences in PX,Y .”
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “, causal Bayesian networks (CBNs)) [148].”
• Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, unknown): “Several criteria and algorithms have been developed [23, 28, 4].”
• Multiple domain user personalization (2011, methodology): “In this paper we propose a flexible model that addresses all of these concerns in a principled fashion, namely by means of directed graphical models [14].”