Probability of benefit in clinical practice
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Probability of benefit in clinical practice
5:27
Tutorial 2
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Tutorial 2
5:49
The rationale of d-separation
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The rationale of d-separation
12:17
Tutorial 1: Stratifying by common effect
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Tutorial 1: Stratifying by common effect
3:47
Conditional Independence
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Conditional Independence
0:13
Marginal Dependence
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Marginal Dependence
0:11
Marginal Independence
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Marginal Independence
0:06
Common Effect
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Common Effect
0:10
Stratifying on common effect
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Stratifying on common effect
0:11
How Markov assumption links DAG to data
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How Markov assumption links DAG to data
12:10
How to read causal diagrams
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How to read causal diagrams
11:57
Nodes, arrows, and chains of dependency
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Nodes, arrows, and chains of dependency
11:32
Adjustment sets in causal analysis
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Adjustment sets in causal analysis
12:30
Causal inference framework
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Causal inference framework
11:04
Propensity score alters the number of outcomes
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Propensity score alters the number of outcomes
11:51
Reasoning
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Reasoning
2:35
Common cause and common effects
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Common cause and common effects
11:49
How to describe independence and association
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How to describe independence and association
12:38
The inverse probability weighting creates pseudo populations
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The inverse probability weighting creates pseudo populations
11:54
How inverse treatment probability makes treatment groups comparable
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How inverse treatment probability makes treatment groups comparable
12:22
How to find treatment effects using potential outcome means
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How to find treatment effects using potential outcome means
12:00
How to find treatment effects through adjustment
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How to find treatment effects through adjustment
11:42
Pearl’s counterfactuals and their meaning
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Pearl’s counterfactuals and their meaning
11:38
Pearl's (yet another) contribution
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Pearl's (yet another) contribution
13:04
Counterfactual questions and data-driven answers
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Counterfactual questions and data-driven answers
11:24
Expected outcomes for each unit
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Expected outcomes for each unit
14:07
How to average stratum-specific effects
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How to average stratum-specific effects
12:00
How stratification makes treatment groups comparable
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How stratification makes treatment groups comparable
11:37
How to define strata for the adjustment formula
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How to define strata for the adjustment formula
11:37
Treatment effects through the back-door adjustment
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Treatment effects through the back-door adjustment
12:20
Steps to find out if treatment effect is identifiable
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Steps to find out if treatment effect is identifiable
11:56
Marginal and conditional contrasts in treatment effects
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Marginal and conditional contrasts in treatment effects
10:52
Treatment effects in causal analysis
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Treatment effects in causal analysis
10:04
Causal mediation analysis formulas
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Causal mediation analysis formulas
12:24
Natural direct and indirect effects
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Natural direct and indirect effects
11:35
Treatment models in causal analysis
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Treatment models in causal analysis
12:17
Outcome models for treatment effects
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Outcome models for treatment effects
11:57
The probability of necessity in causal analysis
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The probability of necessity in causal analysis
11:18
Attributable proportions in causal analysis
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Attributable proportions in causal analysis
12:12
How to block non-causal paths
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How to block non-causal paths
10:50
Causal and non-causal paths
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Causal and non-causal paths
11:00
Three effects of mediation analysis
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Three effects of mediation analysis
11:28
How to find treatment effects
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How to find treatment effects
12:30
The back-door criterion
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The back-door criterion
12:30
Observed and counterfactual risks
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Observed and counterfactual risks
12:08
Causal inferences course
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Causal inferences course
2:15
What is confounding?
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What is confounding?
2:40
How to picture association
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How to picture association
7:42