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A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables
Published on 2007-10-084396 Views
We present an algorithm for causal structure discovery suited in the presence of continuous variables. We test a version based on partial correlation that is able to recover the structure of a recursi
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Presentation
A Partial Correlation-Based Algorithm to Causal Structure Learning00:00
Outline pt 100:07
Outline pt 200:35
Causal Inference and Causal Graphs pt 100:37
Causal Inference and Causal Graphs pt 201:17
Usefulness of a Causal Model pt 101:32
Usefulness of a Causal Model pt 202:00
Usefulness of a Causal Model pt 302:12
Illustration: Simpson’s Paradox pt 102:29
Illustration: Simpson’s Paradox pt 202:58
Properties of the Dataset pt 103:43
Properties of the Dataset pt 204:09
Properties of the Dataset pt 304:41
Outline pt 304:46
Structure Learning Algorithms pt 104:51
Structure Learning Algorithms pt 205:38
Distinguishing Causes from Effects pt 106:13
Distinguishing Causes from Effects pt 207:14
Typical Structure Learning Algorithm07:38
Continuity pt 108:49
Continuity pt 209:41
Outline pt 409:57
Total Conditioning (TC) Algorithm pt 110:02
Total Conditioning (TC) Algorithm pt 211:32
Total Conditioning (TC) Algorithm pt 311:35
The Alarm Network12:07
Results: Alarm, Errors Against Sample Size12:48
Results: Alarm, Run Time Against Sample Size13:59
Conclusion pt 114:37
Conclusion pt 215:17
Conclusion pt 315:42
Thank You for Your Attention!16:06
The Alarm Network (a)17:57
Total Conditioning (TC) Algorithm pt 2 (a)19:31