Zobrazeno 1 - 10
of 438
pro vyhledávání: '"Janzing, P."'
Autor:
Okati, Nastaran, Mejia, Sergio Hernan Garrido, Orchard, William Roy, Blöbaum, Patrick, Janzing, Dominik
Recent work conceptualized root cause analysis (RCA) of anomalies via quantitative contribution analysis using causal counterfactuals in structural causal models (SCMs). The framework comes with three practical challenges: (1) it requires the causal
Externí odkaz:
http://arxiv.org/abs/2406.05014
Autor:
Quintas-Martinez, Victor, Bahadori, Mohammad Taha, Santiago, Eduardo, Mu, Jeff, Janzing, Dominik, Heckerman, David
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024
Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy tha
Externí odkaz:
http://arxiv.org/abs/2404.08839
Autor:
Montagna, Francesco, Mastakouri, Atalanti A., Eulig, Elias, Noceti, Nicoletta, Rosasco, Lorenzo, Janzing, Dominik, Aragam, Bryon, Locatello, Francesco
When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of thei
Externí odkaz:
http://arxiv.org/abs/2310.13387
Autor:
Faller, Philipp M., Vankadara, Leena Chennuru, Mastakouri, Atalanti A., Locatello, Francesco, Janzing, Dominik
As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconceptions about generating processes regarding noise distributions, model cl
Externí odkaz:
http://arxiv.org/abs/2307.09552
Understanding the causal relationships among the variables of a system is paramount to explain and control its behaviour. Inferring the causal graph from observational data without interventions, however, requires a lot of strong assumptions that are
Externí odkaz:
http://arxiv.org/abs/2305.09565
If $X,Y,Z$ denote sets of random variables, two different data sources may contain samples from $P_{X,Y}$ and $P_{Y,Z}$, respectively. We argue that causal discovery can help inferring properties of the `unobserved joint distributions' $P_{X,Y,Z}$ or
Externí odkaz:
http://arxiv.org/abs/2305.06894
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invarian
Externí odkaz:
http://arxiv.org/abs/2305.05832
In aggregated variables the impact of interventions is typically ill-defined because different micro-realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causa
Externí odkaz:
http://arxiv.org/abs/2304.11625
Probabilities of Causation play a fundamental role in decision making in law, health care and public policy. Nevertheless, their point identification is challenging, requiring strong assumptions such as monotonicity. In the absence of such assumption
Externí odkaz:
http://arxiv.org/abs/2304.02023
Autor:
Kirsti M. Jakobs, Karlijn J. van den Brule-Barnhoorn, Jan van Lieshout, Joost G. E. Janzing, Wiepke Cahn, Maria van den Muijsenbergh, Marion C. J. Biermans, Erik W. M. A. Bischoff
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract General practitioners (GPs) are often unaware of antipsychotic (AP)-induced cardiovascular risk (CVR) and therefore patients using atypical APs are not systematically monitored. We evaluated the feasibility of a complex intervention designed
Externí odkaz:
https://doaj.org/article/dcad5e9e77ff42b8b9bcaab946bb2230