Zobrazeno 1 - 10
of 307
pro vyhledávání: '"Janzing Dominik"'
Publikováno v:
Journal of Causal Inference, Vol 12, Iss 1, Pp 8-14 (2024)
Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking “what qualifies an action for being an intervent
Externí odkaz:
https://doaj.org/article/6b766cd7ad5d4f76979c9c10cc9f34ee
Autor:
Janzing Dominik
Publikováno v:
Journal of Causal Inference, Vol 9, Iss 1, Pp 285-301 (2021)
The principle of insufficient reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle (MaxEnt) generalizes PIR to the case where stati
Externí odkaz:
https://doaj.org/article/d64760b3213841768a6f48f9d1efc458
We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a pair of variables that has been dropped when learning the causal model. To this end, we use the "Leave-One-Variable
Externí odkaz:
http://arxiv.org/abs/2411.05625
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 d
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:
Janzing Dominik, Schölkopf Bernhard
Publikováno v:
Journal of Causal Inference, Vol 6, Iss 1, Pp 362-378 (2018)
We study a model where one target variable Y$Y$ is correlated with a vector X:=(X1,…,Xd)$\textbf{X}:=(X_1,\dots,X_d)$ of predictor variables being potential causes of Y$Y$. We describe a method that infers to what extent the statistical dependences
Externí odkaz:
https://doaj.org/article/86ae8b1e25ca494eb17c912a00aba934
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