Causality

By Illtyd Trethowan. 1938

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In Causal Inference & Discovery

Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, background): “Achieving this requires to answer questions, such as “what would be the impact on the system if this variable were changed from value x to y?”, which cannot be answered without causal knowledge [33].”
Deep Kalman Filters (2015, methodology): “Pearl (2009) framed the problem of counterfactual estimation in the language of graphical models and do -calculus.”
Avoiding Discrimination through Causal Reasoning (2017, methodology): “For background on causal graphs see [11].”
On the Fairness of Causal Algorithmic Recourse (2020, methodology): “which follows from applying do-calculus (Pearl 2009) since”
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning (2020, background): “Moreover, causality has been historically studied from the perspective of probabilistic and causal reasoning (Pearl, 2009), cognitive psychology (Grifﬁths & Tenenbaum, 2005), and more recently in the context of machine learning (Goyal et al., 2019b; Sch ¨ olkopf, 2019; Baradel et al., 2019; Bakhtin…”
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, background): “For example, two countries might have the same true mortality rate, but their CFRs can differ significantly if one of them only tests patients with severe symptoms (which are therefore more likely to die), see Rajgor et al. (2020). Similar problems have been addressed in the causal inference literat”
CLEVRER: CoLlision Events for Video REpresentation and Reasoning (2019, background): “…Fragkiadaki et al., 2016; Battaglia et al., 2016; Chang et al., 2017; Agrawal et al., 2016; Finn et al., 2016; Shao et al., 2014; Fire & Zhu, 2016; Pearl, 2009; Ye et al., 2018), either directly from images (Finn et al., 2016; Ebert et al., 2017; Watters et al., 2017; Lerer et al., 2016; Mottaghi…”
Learning Neural Causal Models from Unknown Interventions (2019, background): “The recovery of the underlying structural causal graph from observational and interventional data is a fundamental problem (Pearl, 1995; 2009).”
Causal Regularization (2019, methodology): “The change of Y caused by setting X to x via interventions is given by Pearl’s backdoor criterion [5] as p(y|do(x)) = ∫ p(y|x, z)p(z)dz.”
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019, background): “These latent factors represent elementary ingredients to the causal mechanism generating X [44, 45].”
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019, methodology): “Pearl’s seminal work on do-calculus Pearl (1995, 2009); Bareinboim and Pearl (2016) lays a foundation for expressing the impact of interventions on probabilistic graphical models – we use it in our work.”
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, background): “They should enable to integrate out nuisance factors (Kumar et al., 2017), to perform interventions, and to answer counterfactual questions (Pearl, 2009; Spirtes et al., 1993; Peters et al., 2017).”
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018, methodology): “…i for j (cid:54) = i ; i.e., intervening on G j does not change G i , i.e, The remaining components of G , i.e., G \ j , are a valid adjustment set (Pearl, 2009) to estimate interventional effects from G j to X based on observational data, i.e., p ( x | do ( G j ← g j )) = p ( x | g j , g \ j ) p…”
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018, background): “This is known as hidden confounding (Rosenbaum and Rubin 1983; Pearl 2009) .”
Learning Independent Causal Mechanisms (2017, methodology): “As an aside, we note that in the alter-a native view of causal models as structural equation models, each of the causal conditionals corresponds to a functional mapping and a noise variable (Pearl, 2000).”
Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference (2015, methodology): “Meanwhile, however, the increasing interdisciplinary field of causal inference has shown that also the latter is possible –even without information about time order– if appropriate assumptions that link causality and statistics are made [1, 2, 3], with applications in biology [4], psychology [5], an”
On causal and anticausal learning (2012, methodology): “In view of this, it is surprising that conditional independence alone does allow us to do causal inference of practical signiﬁcance, as implemented by the PC and FCI algorithms (Spirtes et al., 1993; Pearl, 2000).”