Causation, Prediction, and Search

By Tom L. Burr. 2003

5,823 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, background): “They should enable to integrate out nuisance factors (Kumar et al., 2018), to perform interventions, and to answer counterfactual questions (Pearl, 2009; Spirtes et al., 2000; Peters et al., 2017).”
Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Following Lorch et al. (2021), we used two variants of Bootstrapping based on the score-based algorithm GES (Bootstrap GES; Chickering, 2002), and the constraint-based algorithm PC (Bootstrap PC; Spirtes et al., 2000), as the internal structure learning routines.”
Differentiable Causal Discovery from Interventional Data (2020, methodology): “The PC algorithm [33] is a classical example that works with observational data.”
An Analysis of the Adaptation Speed of Causal Models (2020, methodology): “Many methods seek to achieve this goal, whether constraint-based such as the PC algorithm (Spirtes et al., 2000) or score-based methods using greedy search (Chickering, 2002) or more recently continuous optimization (Zheng et al.”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Under Markov and faithfulness assumptions (Pearl 2000), the PC algorithm (Spirtes et al. 2000) exploits conditional dependencies to recover the Markov equivalence class of G.”
Learning Optimal Fair Policies (2018, background): “For more details on causal graphical models see, e.g., (Spirtes, Glymour, and Scheines 2001; Pearl 2009).”
Causal Reasoning for Algorithmic Fairness (2018, methodology): “In particular, in this paper we will make use primarily of the structural causal model (SCM) framework advocated by [29], which shares much in common with the approaches by [33] and [39].”
Fair Inference on Outcomes (2017, background): “Causal relationships are often represented by graphical causal models (Spirtes, Glymour, and Scheines 2001; Pearl 2009).”
Causal Models for Dynamical Systems (2020, background): “…is some understanding of the eﬀects of hidden variables on observed distributions, on the identiﬁcation of causal eﬀects and on causal discovery [e.g. Spirtes et al., 2000, Pearl, 2009, Shpitser, 2018, Richardson and Spirtes, 2002, Hern´an and Robins, 2006, Evans, 2015, Richardson et al., 2017,…”
Learning Neural Causal Models from Unknown Interventions (2019, methodology): “…learning causal structure based on observational data (Chickering, 2002; Tsamardinos et al., 2006; Goudet et al., 2017; Hauser & Bühlmann, 2012; Spirtes et al., 2000; Sun et al., 2007; Zhang et al., 2012; Shimizu et al., 2006; Hoyer et al., 2009; Daniusis et al., 2012; Budhathoki & Vreeken,…”
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019, background): “They should allow to integrate out nuisance factors [22], to perform interventions and to answer counterfactual questions [39, 44, 40].”
Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Therefore, standard constraintbased algorithms such as PC or FCI (Spirtes et al., 1993) may not be able to reveal the true causal structure.”
Causal Reasoning from Meta-reinforcement Learning (2019, methodology): “There is a rich literature on formal approaches for defining and performing causal reasoning (Pearl, 2000; Spirtes et al., 2000; Dawid, 2007; Pearl et al., 2016).”
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, background): “They should enable to integrate out nuisance factors [32], to perform interventions, and to answer counterfactual questions [44, 53, 46].”
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018, methodology): “We view disentanglement as a property of a causal process (Spirtes et al., 1993; Pearl, 2009) responsible for the data generation, as opposed to only a heuristic characteristic of the encoding.”
The hardness of conditional independence testing and the generalised covariance measure (2018, methodology): “Constraint-based or independence-based methods [41, 52, 43] apply a series of conditional independence tests in order to learn causal structure from observational data.”
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes (2018, background): “Many extant causal discovery algorithms use conditional independence tests to infer the underlying causal structure over a set of variables from observational data [Pearl, 2000, Spirtes et al., 2000, Hoyer et al., 2009].”
Causal Generative Neural Networks (2017, methodology): “We compare CGNN to the PC algorithm [Spirtes et al. , 2000], the score-based methods GES [Chickering, 2002], LiNGAM [Shimizu et al. , 2006], causal additive model (CAM) [Peters et al. , 2014] and with the pairwise methods ANM and Jarfo.”
Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “Causal modeling (Spirtes et al., 2001; Pearl, 2000) of time series data has been widely applied in many fields such as econometrics (Ghysels et al.”