IMPROVED REVISION OF RANKING FUNCTIONS FOR THE GENERALIZATION OF BELIEF IN THE CONTEXT OF UNOBSERVED VARIABLES

Scientific inquiry seeks to understand natural phenomena by understanding their underlying processes, i.e., by identifying cause and effect. In addition to mere scientific curiosity, an understanding of cause and effect relationships is necessary to predict the effect of changing dynamical regimes and for the attribution of extreme events to potential causes. It is thus an important question to ask how, in cases where controlled experiments are not feasible, causation can still be inferred from the statistical dependencies in observed time series.A central obstacle for such an inference is the potential existence of unobserved causally relevant variables. Arguably, this is more likely to be the case than not, for example unmeasured deep oceanic variables in atmospheric processes. Unobserved variables can act as confounders (meaning they are a common cause of two or more observed variables) and thus introduce spurious, i.e., non-causal dependencies. Despite these complications, the last three decades have seen the development of so-called causal discovery algorithms (an example being FCI by Spirtes et al., 1999) that are often able to identify spurious associations and to distinguish them from genuine causation. This opens the possibility for a data-driven approach to infer cause and effect relationships among climate variables, thereby contributing to a better understanding of Earth's complex climate system.These methods are, however, not yet well adapted to some specific challenges that climate time series often come with, e.g. strong autocorrelation, time lags and nonlinearities. To close this methodological gap, we generalize the ideas of the recent PCMCI causal discovery algorithm (Runge et al., 2019) to time series where unobserved causally relevant variables may exist (in contrast, PCMCI made the assumption of no confounding). Further, we present preliminary applications to modes of climate variability.

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Corporate Governance to Prevent Financial Distress Evidence from Corporate Governance Perceptions Index of Indonesian Companies

Indonesian Journal Of Business And Economics ◽

10.25134/ijbe.v1i1.1360 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Teti Rahmawati ◽

Yana Hendriyana

Keyword(s):

Corporate Governance ◽

Financial Distress ◽

Sampling Method ◽

Stock Exchange ◽

Company Size ◽

Purposive Sampling ◽

Good Corporate Governance ◽

Unobserved Variables

This study aims to determine the influence of Good Corporate Governance (GCG), Company Size, Liquidity, and Rentability on Financial Distress of companies listed on Corporate Governance Perception Index (CGPI) partially and simultaneously. �The population of this research is companies listed on the Indonesian Stock Exchange (BEI) and Corporate Governance Perception ranks starting from 2013 to 2016. Based on the criteria above, 59 companies are selected. The sampling of this research is taken by using purposive sampling method from the population with a target of several considerations. The result shows that Good Corporate Governance does not significantly influence Financial Distress, Company Size negatively affects Financial Distress, Liquidity positively affects Financial Distress, and Rentability positively affects Financial Distress.� Good Corporate Governance, Company Size, Liquidity, and Rentability partially influence Financial Distress with coefficient determination is 92,25% while 2,75% is explained by other unobserved variables in outside the model.

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On learning linear ranking functions for beam search

Proceedings of the 24th international conference on Machine learning - ICML '07 ◽

10.1145/1273496.1273628 ◽

2007 ◽

Cited By ~ 12

Author(s):

Yuehua Xu ◽

Alan Fern

Keyword(s):

Beam Search ◽

Ranking Functions

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Explaining monotonic ranking functions

Proceedings of the VLDB Endowment ◽

10.14778/3436905.3436922 ◽

2020 ◽

Vol 14 (4) ◽

pp. 640-652

Author(s):

Abraham Gale ◽

Amélie Marian

Keyword(s):

Decision Making ◽

Selection Process ◽

A Priori ◽

Data Distribution ◽

Synthetic Data ◽

Decision Processes ◽

Decision Makers ◽

Ranking Functions ◽

High School Admissions ◽

Parameter Values

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

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