نتایج جستجو برای: causal
تعداد نتایج: 63359 فیلتر نتایج به سال:
In our data-‐rich world, key medical decisions, ranging from a regulator's decision to curtail a drug to patient-‐specific treatment choices require optimal consideration of myriad inputs. Statistical/epidemiological methods that can harness real-‐world medical data in useful ways do exis...
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the id...
System dynamics (SD) is a well-known methodology for analyzing the structure and the behavior of business systems and the environment. The utility of SD has been proven in numerous cases. However, decision makers have reservations using SD to support their decision processes. The reasons can be a) decision makers have problems to design a causal model under given time constraints (aspect of met...
This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cr...
We introduce Joint Causal Inference (JCI), a powerful formulation of causal discovery from multiple datasets that allows to jointly learn both the causal structure and targets of interventions from statistical independences in pooled data. Compared with existing constraint-based approaches for causal discovery from multiple data sets, JCI offers several advantages: it allows for several differe...
Earlier studies in 1980s found no causal links between IT investments and productivity, since then a growing body of research has been investigating such links at a much finer-level of analysis. Yet the results have been inconclusive. We attribute the mixed findings to an incomplete causal chain analysis, specifically the exclusion of key constructs such as strategic alignment which would allow...
Discrimination discovery is to unveil discrimination against a specific individual by analyzing the historical dataset. In this paper, we develop a general technique to capture discrimination based on the legally grounded situation testing methodology. For any individual, we find pairs of tuples from the dataset with similar characteristics apart from belonging or not to the protected-by-law gr...
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