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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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Beyond Structural Causal Models: Causal Constraints Models [PDF]

, 2018
Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), and prove that CCMs do capture the ...
Blom, T., Bongers, S., Mooij, J.M.
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Causal Consistency of Structural Equation Models [PDF]

, 2017
Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations ...
Rubenstein, P.K., Weichwald, S., Bongers, S., Mooij, J.M., Janzing, D., Grosse-Wentrup, M., Schölkopf, B.   +6 more
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Algorithmic recourse in sequential decision-making for long-term fairness [PDF]

Frontiers in Big Data
Long-term fairness in sequential decision-making is critical yet challenging, as decisions at each time step influence future opportunities and outcomes, potentially exacerbating existing disparities over time.
Francisco Gumucio, Lu Zhang
doaj   +2 more sources

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, Orsi, Renzo, Wunsch, Guillaume   +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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Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models

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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Modeling causal structures [PDF]

European Journal for Philosophy of Science, 2012
The Lotka-Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra's and Umberto D'Ancona's original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling ...
Scholl, Raphael, Räz, Tim
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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, Dhami, Devendra Singh, Rothkopf, Constantin A., Kersting, Kristian   +3 more
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