• Title/Summary/Keyword: causal graph

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Modeling of Event Development based on the Structured Causal Graph (Structured Causal Graph에 기반한 이벤트 전개 방법의 개발)

  • 지세진;우영욱;황원택;최운돈;박종희
    • Proceedings of the Korean Information Science Society Conference
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    • pp.427-429
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    • 2001
  • 지금까지 가상현실을 이용한 여러 가지 시스템들이 제안되어 왔다. 하지만 이러한 시스템들은 사용자에게 몰입감을 줄 수 있는 예측하기 힘든 다양한 상황을 제공하기보다는 미리 정해진 시나리오를 따라 고정된 형태의 흐름을 가지는 문제점이 있었다. 이를 해결하기 위해가상세계 내에서의 오브젝트의 행동이나 이벤트의 전개를 위한 여러 가지 방법들이 제안되어왔다. 하지만 이 방법들 역시 방대한 탐색 공간이나 한정된 범위내에서만 자율적인 움직임이 가능한 점 등의 문제점을 가지고 있다. 본 논문에서는 이를 해결하기 위하여 Causality에 기반한 이벤트의 전개모델을 제안한다. 이를 위해 본 논문은 먼저 frame구조를 이용하여 정형화한 Structured Causal Graph를 제안하고, 구성되어진 Structured Causal Graph를 이용하여 이벤트를 전개해나가는 방법을 제시한다.

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Covariate selection criteria for controlling confounding bias in a causal study (인과연구에서 중첩편향을 제거하기 위한 공변량선택기준)

  • Thepepomma, Seethad;Kim, Ji-Hyun
    • The Korean Journal of Applied Statistics
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    • v.29 no.5
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    • pp.849-858
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    • 2016
  • It is important to control confounding bias when estimating the causal effect of treatment in an observational study. We illustrated that the covariate selection in the causal inference is different from the variable selection in the ANCOVA model. We then investigated the three criteria of covariate selection for controlling confounding bias, which can be used when we have inadequate information to draw a complete causal graph. VanderWeele and Shpitser (2011) proposed one of them and claimed it was better than the other two. We show by example that their criterion also has limitations and some disadvantages. There is no clear winner; however, their criterion is better (if some correction is made on its condition) than the other two because it can remove the confounding bias.

Causal study on the effect of survey methods in the 19th presidential election telephone survey (19대 대선 전화조사에서 조사방법 효과에 대한 인과연구)

  • Kim, Ji-Hyun;Jung, Hyojae
    • The Korean Journal of Applied Statistics
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    • v.30 no.6
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    • pp.943-955
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    • 2017
  • We investigate and estimate the causal effect of the survey methods in telephone surveys for the 19th presidential election. For this causal study, we draw a causal graph that represents the causal relationship between variables. Then we decide which variables should be included in the model and which variables should not be. We explain why the research agency is a should-be variable and the response rate is a shouldnot-be variable. The effect of ARS can not be estimated due to data limitations. We have found that there is no significant difference in the effect of the proportion of cell phone survey if it is less than about 90 percent. But the support rate for Moon Jae-in gets higher if the survey is performed only by cell phones.