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A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables
Published on Feb 4, 20254396 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