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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Martin Peters
, Peter Bühlmann, Nicolai Meinshausen
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Dive into the research topics of 'Causal inference by using invariant prediction: identification and confidence intervals'. Together they form a unique fingerprint.
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Mathematics
Causal Model
96%
Causal Inference
92%
Confidence interval
56%
Prediction
54%
Invariant
37%
Predictors
31%
Invariance
29%
Structural Equation Model
22%
Model
22%
Model Misspecification
21%
Gene
16%
Scenarios
15%
Robustness
14%
Experiment
12%
Valid
12%
Sufficient
12%
Perturbation
11%
Relationships
10%
Business & Economics
Confidence Interval
100%
Causal Inference
87%
Causal Model
80%
Prediction
55%
Invariance
39%
Predictors
36%
Structural Equation Model
26%
Model Misspecification
21%
Predictive Accuracy
21%
Robustness
21%
Perturbation
19%
Gene
15%
Scenarios
10%
Experiment
10%