Causality: Interventions | Part B
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2 سال پیش
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Tutorial on causal inference, covering
Tutorial on causal inference, covering the basics of interventional reasoning. Topics: causal effect; Simpson's paradox; interventional distributions; causal effect rule; Markovian and semi-Markovian causal models; identifiability criteria such as backdoor, frontdoor and the do-calculus; and why the causal effect may not be identifiable. The computation/estimation of causal effect is discussed in both an idealized setting (complete model) and a practical setting (causal graph + observational data).
00:00 Introduction
00:11 The Identifiability Problem: Input-Output
03:00 Types of Data: Observational and Interventional
04:09 Why The Causal Effect May Not Be Identifiable
10:32 Types of Causal Graphs: Markovian and Semi-Markovian
12:07 Identifiability Criteria
13:19 Causal Effect Rule
15:33 Backdoor Criterion
25:16 Frontdoor Criterion
27:10 Incompleteness of Backdoor Criterion
29:20 The Do-Calculus
40:22 Concluding Remarks
--- Slides available at: http://web.cs.ucla.edu/~darwiche/caus...
00:00 Introduction
00:11 The Identifiability Problem: Input-Output
03:00 Types of Data: Observational and Interventional
04:09 Why The Causal Effect May Not Be Identifiable
10:32 Types of Causal Graphs: Markovian and Semi-Markovian
12:07 Identifiability Criteria
13:19 Causal Effect Rule
15:33 Backdoor Criterion
25:16 Frontdoor Criterion
27:10 Incompleteness of Backdoor Criterion
29:20 The Do-Calculus
40:22 Concluding Remarks
--- Slides available at: http://web.cs.ucla.edu/~darwiche/caus...
2 سال پیش
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