Results 11 to 20 of about 10,579,206 (300)

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
doaj   +2 more sources

Understanding the European energy crisis through structural causal models [PDF]

Nature Communications
Natural gas supplies in Europe were disrupted and energy prices soared in the context of Russia’s invasion of Ukraine. Electricity prices in France experienced the largest relative increase among European countries, even though the share of natural gas ...
Sarah Schreyer, Anton Tausendfreund, Florian Immig, Ulrich Oberhofer, Julius Trebbien, Aaron Praktiknjo, Benjamin Schäfer, Dirk Witthaut   +7 more
doaj   +2 more sources

Learning Causal Abstractions of Linear Structural Causal Models

CoRR
The need for modelling causal knowledge at different levels of granularity arises in several settings. Causal Abstraction provides a framework for formalizing this problem by relating two Structural Causal Models at different levels of detail. Despite increasing interest in applying causal abstraction, e.g.
Massidda, Riccardo, Magliacane, Sara, Bacciu, Davide   +2 more
core   +7 more sources

Non-causal explanation in science [PDF]

, 2013
Non-causal and causal explanation in science are unified under an extension of James Woodward's manipulationist account of causal explanation. Scientific explanation is about capturing and representing the modal structure of the world.
Pexton, Mark
core   +5 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
openaire   +3 more sources

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
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +1 more source

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, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou   +7 more
openaire   +3 more sources

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
openaire   +2 more sources