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Algorithmic Accountability in Context. Socio-Technical Perspectives on Structural Causal Models [PDF]
Frontiers in Big Data, 2021 The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an ...
Nikolaus Poechhacker, Severin Kacianka
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Review of Causal Discovery Methods Based on Graphical Models
Frontiers in Genetics, 2019 A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even ...
Clark Glymour, Kun Zhang, Peter Spirtes
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Ecological InformaticsUnderstanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making.
Mallika Kliangkhlao +7 more
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Causality in Econometric Modeling. From Theory to Structural Causal Modeling [PDF]
SSRN Electronic Journal, 2020 This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed.
Mouchart, Michel +2 more
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On the pitfalls of Gaussian likelihood scoring for causal discovery
Journal of Causal Inference, 2023 We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
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BMC Medical Research Methodology, 2022 Background Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted.
Riko Kelter
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Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”
Journal of Causal Inference, 2022 In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic ...
Pearl Judea
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Causal Explanations of Structural Causal Models
, 2021 In explanatory interactive learning (XIL) the user queries the learner, then the learner explains its answer to the user and finally the loop repeats. XIL is attractive for two reasons, (1) the learner becomes better and (2) the user's trust increases. For both reasons to hold, the learner's explanations must be useful to the user and the user must be ...
Zečević, Matej +3 more
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Learning Latent Structural Causal Models
CoRR, 2022 Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given.
Jithendaraa Subramanian +7 more
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Inferring causal phenotype networks using structural equation models
Genetics Selection Evolution, 2011 Phenotypic traits may exert causal effects between them. For example, on the one hand, high yield in dairy cows may increase the liability to certain diseases and, on the other hand, the incidence of a disease may affect yield negatively.
de los Campos Gustavo +5 more
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