Causal inference and the data-fusion problem

By Elias Bareinboim, Judea Pearl. 2016

750 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Causal Reasoning for Algorithmic Fairness (2018, background): “But selection bias cuts deeper than that, and more recently causal knowledge has been formally brought in to understand the role of such biases [2, 40].”
Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, methodology): “Current methods require that the structure of data be explicitly specified [3] or at best be guided by subject matter knowledge [11].”
Causal Models for Dynamical Systems (2020, background): “[2009], and Bauer et al. [2016] discuss the relation between causality and the arrow of time.”
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.”
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017, background): “…domain, but much more accurate ones in the target domain. across the different domains, allows one to transfer knowledge from one domain to the other in order to address the problem of domain adaptation [Spirtes et al., 2000, Storkey, 2009, Schölkopf et al., 2012, Bareinboim and Pearl, 2016].”
Group invariance principles for causal generative models (2017, background): “leaning, addressing covariate-shift or changes in the input distribution (Zhang et al., 2013, 2015; Peters et al., 2016; Bareinboim and Pearl, 2016; RojasCarulla et al., 2015), and makes causal generative models highly relevant in machine learning.”
Balanced Linear Contextual Bandits (2018, background): “There is a number of recent works which approach contextual bandits through the framework of causality (Bareinboim, Forney, and Pearl 2015), (Bareinboim and Pearl 2015), (Forney, Pearl, and Bareinboim 2017), (Lattimore, Lattimore, and Reid 2016).”
Estimation Considerations in Contextual Bandits (2017, background): “There is a number of recent works which approach contextual bandits through the framework of causality [14, 15, 31, 41].”
Fairness in Decision-Making - The Causal Explanation Formula (2018, positive): “We build on the language of causality (Pearl 2000; Halpern 2000; Bareinboim and Pearl 2016) to express direct and indirect discrimination through the different paths connecting the protected attribute X and the outcome Y in the underlying causal diagram (see Fig.”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, background): “It can be used both for re-adjusting learned policies to circumvent environmental changes and for controlling bias due to non-representative samples (Bareinboim and Pearl, 2016).”
Algorithmic Fairness (2020, background): “Another approach to improve fairness using causal reasoning is to provide an understanding of how to perform imputation of missing values or how to repair a dataset that contains sample or selection bias [10, 127].”
Meaningful Explanations of Black Box AI Decision Systems (2019, methodology): “…speciﬁcally to explain ML models, with a few exceptions dealing on speciﬁc issues, such as discrimination inference (Zhang and Bareinboim 2018; Bonchi et al. 2017; Caliskan, Bryson, and Narayanan 2017; Caravagna et al. 2016) and reasoning on sample selection bias (Bareinboim and Pearl 2016).”
Equality of Opportunity in Classification: A Causal Approach (2018, background): “For a more detailed discussion on SCMs, please refer to [16, 2].”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, background): “Several criteria and algorithms have been developed [23, 28, 4].”
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, background): “The requirement for and issues surrounding this assumption are detailed elsewhere.(31,32)”
Counterfactual Data-Fusion for Online Reinforcement Learners (2017, background): “This challenge is not without precedent, as recent studies have investigated dataset transportability, though in offline domains [3].”