Causality : Models , Reasoning , and Inference

By Peter M. Bentler, D. J. Heslenfeld, Peter C. M. Molenaar, R. Mcdonald. 2002

Subfields: Probabilistic & Bayesian Methods, Causal Inference & Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

In Causal Inference & Discovery

Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “In addition to the observational data we used in Section 6.3, Sachs et al. (2005) also provided ﬂow cytometry data under different experimental conditions, where the T-cells were perturbed with some reagents; this effectively corresponds to interventional data (Pearl, 2009).”
Gradient-Based Neural DAG Learning (2019, methodology): “In the original method named NOTEARS [18], the directed graph is encoded as a weighted adjacency matrix W which represents coefficients in a linear structural equation model (SEM) [7].”
Locating and Editing Factual Associations in GPT (2022, background): “The grid of states (Figure 1) forms a causal graph (Pearl, 2009) describing dependencies between the hidden variables.”
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators (2024, background): “Note that the (baseline) “instruction difficulty” is not caused by “model” but conditioning on it can enhance the precision of estimates in regression analysis by reducing unexplained variability Pearl (2009).”
Counterfactual Fairness (2017, methodology): “We denote it by YZ←z(u) [28], and sometimes as Yz if the context of the notation is clear.”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “= x) where do(X = x) denotes the operation of setting the value of X to be equal to x (Pearl 2000).”
Discovering Causal Signals in Images (2016, background): “rections for future research. Our code is available at http://github.com/lopezpaz/ visual_causation. 2 Observational causal inference Randomized experiments are the gold standard for causal inference [12]. Like a child may drop a toy to probe the nature of gravity, experiments rely on interventions ”
Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, methodology): “In the following we present several properties regarding the counterfactual statement, which are proved to be held in the context of Markovian model [10].”
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].”
Path-Specific Counterfactual Fairness (2018, methodology): “The suggestion is to view unfairness as the presence of an unfair causal effect of the sensitive attribute on the decision, as already done in Pearl (2000) and Pearl et al. (2016), e.g. , to analyse the case of Berkeley’s alleged sex bias in graduate admission.”
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (2016, background): “By using the do -calculus, the total causal e ﬀ ect of X on Y is deﬁned as follows (Pearl 2009).”
Causal Inference through a Witness Protection Program (2014, background): “For binary variables, the ACE is defined as E[Y | do(X = 1)]− E[Y | do(X = 0)] = P (Y = 1 | do(X = 1))− P (Y = 1 | do(X = 0)), (1) where do(·) is the operator of Pearl (2000), denoting distributions where a set of variables has been intervened on by an external agent.”
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].”
Learning explanations that are hard to vary (2020, background): “This postulate has appeared in various forms in the literature [Haavelmo, 1943, Simon, 1953, Hurwicz, 1962, Pearl, 2009, Schölkopf et al., 2012].14 Based on this insight, it was proposed that regression based on causal features should presents desirable invariance and robustness properties [Mooij…”
A theory of independent mechanisms for extrapolation in generative models (2020, background): “(1) as a structural causal model (Pearl 2000) with causal graph Z → V → X .”
Causal Models for Dynamical Systems (2020, background): “…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, Hyttinen et al., 2012,…”
Recurrent Independent Mechanisms (2019, background): “In the causality community, this is often considered a prerequisite of being able to perform localized interventions upon variables determined by such models (Pearl, 2009).”