Causal Inference

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.


Paper Code Results Date Stars


Task Papers Share
Causal Inference 500 53.08%
Decision Making 42 4.46%
Time Series 34 3.61%
BIG-bench Machine Learning 33 3.50%
Causal Discovery 32 3.40%
Fairness 25 2.65%
Recommendation Systems 23 2.44%
Association 14 1.49%
Selection bias 13 1.38%


Component Type
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