A Framework for Crowd-Based Causal Analysis of Open Data

Song, Jing; Oyama, Satoshi; Kurihara, Masahito

doi:10.1109/smc.2018.00376

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Complex causal models and analyses involving huge correlation matrices are more common in social sciences than simple causal models with one correlation coefficient (Trafimow 2017). Song et al (2018) framed causal relationships by combining collective intelligence with modern machine learning methods, devising an efficient technique for causal analysis of open data.…”

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confidence: 99%

Impacts of the war on prices of Ukrainian wheat

Novotná,

Rowland,

Janek

2023

Agric. Econ. - Czech

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The Russian-Ukrainian armed conflict significantly affected wheat production and its export from Ukraine, mainly during the war outbreak. Since both countries rank among the major global wheat producers, the warfare disrupted wheat supplies, hastily pushing the prices. Based on the analysed data, we carried out research using multilayer perceptron networks. The findings suggest the biggest price increase between February and March 2022, witnessing wheat prices at about 1 400 USD per t. We predict a decline to the pre-war values until the end of 2025, estimating its rates between 600 USD and 800 USD per t. This price slump may involve signing an agreement on unblocking Ukrainian seaports, which would restore wheat exports. Yet, our survey is confined to historical data, which do not suggest any dramatic event that would alarmingly sway wheat prices.

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confidence: 99%

Impacts of the war on prices of Ukrainian wheat

Novotná,

Rowland,

Janek

2023

Agric. Econ. - Czech

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Identification of Possible Common Causes by Intrinsic Dimension Estimation

Song

Oyama

Kurihara

2019

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)

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The effect of confounding factors cannot be ignored in real world causal discovery tasks. A common cause is a general confounder between two variables. In this paper, we propose using intrinsic dimension estimation as a necessary condition to determine a possible common cause for two variables. Simulated application showed that the proposed method worked well for both linear and non-linear functions. Testing using different types of noise showed that it generally worked well for different types of added noise. In particular, it worked better than a kernel-based conditional independence test for Poisson noise. Testing of how the estimated intrinsic dimension is affected by different types of distributions showed that the estimated dimension is nearly not affected by the type of distribution. Simulation of mixed pattern showed that the proposed method can still tell a possible common cause when it is mixed with causal relationship. Finally, experiments using variables from the CauseEffectPairs dataset showed that the proposed method can give correct inferred results for real world data.Index Terms-common cause identification, intrinsic dimension, conditional independence test

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A Framework for Crowd-Based Causal Analysis of Open Data

Cited by 2 publications

References 24 publications

Impacts of the war on prices of Ukrainian wheat

Impacts of the war on prices of Ukrainian wheat

Identification of Possible Common Causes by Intrinsic Dimension Estimation

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