Causation

By J. Gerring. 2005

Subfields: Causal Inference & Discovery, Philosophy

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Citations in the corpus

Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning (2019, background): “Counterfactual and causal inferences have long been considered two sides of the same coin [Hume, 1739/1978; Lewis, 1973].”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “However, as argued by Lewis [98], the definition due to Hume is in fact about counterfactuals, rather than dependence alone.”
Causal Reasoning for Algorithmic Fairness (2018, methodology): “A sufficient condition for Y ⊥ A is that there are no causal paths between Y and A (this condition is also necessary under the assumption of faithfulness discussed by [39]).”
Causal Inference through a Witness Protection Program (2014, methodology): “Without knowledge of the causal graph, assumptions such as faithfulness [22] are used to infer it.”
Towards Causal Representation Learning (2021, background): “, changing Ui, setting fi (and thus Xi) to a constant, or changing the functional form of fi (and thus the dependency of Xi on its parents) [237, 183].”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “Therefore, standard constraint-based algorithms such as the PC and SGS algorithms [Spirtes et al. , 2001] may not be able to reveal the true causal structure.”
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”
A Kernel Test for Three-Variable Interactions (2013, methodology): “In the PC algorithm [13, 14, 15], a V-structure (a directed graphical model with two independent parents pointing to a single child) is detected when an independence test between the parent variables accepts the null hypothesis, while a test of dependence of the parents conditioned on the child reje”
Causal Inference Using the Algorithmic Markov Condition (2008, methodology): “Causal inference using the Markov condition and the faithfulness assumption has been implemented as causal learning algorithms [2].”
Learning Representations for Counterfactual Inference (2016, background): “The problem of causal inference is often framed in terms of counterfactual questions (Lewis, 1973; Rubin, 1974; Pearl, 2009) such as “Would this patient have lower blood sugar had she received a different medication?””
Telling cause from effect based on high-dimensional observations (2009, background): “However, the methods are fundamentally unable to distinguish between DAGs that induce the same set of dependencies (Markov-equivalent graphs).”
Exposing the probabilistic causal structure of discrimination (2015, background): “The most complete known counterfactual theory of causation is due to David Lewis [23] and exploits a possible world semantics to state truth conditions for counterfactuals in terms of similarity among possible worlds: one possible”
Causal Discovery with Reinforcement Learning (2019, methodology): “If all the functions fi are linear and all the noises ni are Gaussian distributed, this model yields the standard linear-Gaussian model family that has been studied in [24, 9, 2, 18].”
Efficient Search for Diverse Coherent Explanations (2019, background): “This is a much looser definition of counterfactual than that used in the causal literature (e.g. Pearl (2000)) and some thought needs to go into the choice of distance function to make the counterfactuals found useful.”
What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research (2021, background): “, information that appeals to the causes of an event; [182, 183]), network (i.”
CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS (2014, methodology): “The special cases of (3) where A and Y are disjoint are known as the gformula [16], themanipulated distribution [26], or the truncated factorization [8].”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Then we can use the standard orientation rules for unshielded triples to orient the edge between Vk and Vl [2, 3]: if Vl and C are independent given a set of variables excluding Vk, then the triple is a V-structure, and we have Vk ← Vl.”
Causal identifiability via Chain Event Graphs (2013, methodology): “Suppose we have a CBN which is a valid expression of our causal model, and that Pearl’s Front Door conditions hold.”